In this work, the authors present a novel method to identify and differentiate between levels of bearing corrosion in soft-started induction motors. This method relies on the computation of the permutation entropy of the stray-flux and current transient signals. In the case of soft-started induction motors, the detection of the fault related patterns is more difficult, due to the harmonics introduced in the signals by those devices. Nevertheless, the experimental results show that the fault studied can be not only identified but also the level of corrosion can be differentiated using the proposed method. The main advantages of this method are that it can be used online and its low computational requirements.

Fault detection in induction motors with noninvasive methods using stray magnetic flux is a convenient way to maintain reliability and reduce maintenance costs. An online monitoring of critical systems like railway transportation, industrial conveyors, or water management could benefit from the rapid identification of incipient failures. One of the principal problems with the fault identification in induction motors is that the characteristic failure signature changes depending on various factors such as motor construction, controller device, load configuration, and rated power. It is common to use artificial intelligence tools like artificial neural networks to identify and classify failures; however, black-box models are not scalable and are unique to each case. In addition, black-box models do not provide insights into the signal relationship among useful signal properties to detect failures. In this work, a fuzzy inference clustering (FIC) method is presented to provide an interpretable classification model to infer fuzzy membership rules, allowing the detection of failures in induction motors with stray magnetic flux signals. The proposed method achieves 100% accuracy in detecting two ball-bearing failures in the outer raceway using axial or radial flux. The obtained model is human-readable, and its real-time implementation requires minimal computational resources, suitable for edge computing applications.

Induction motors are robust machines that are present in various sectors, including industry, transportation, mining, and others, employing a wide range of applications such as pumps, presses, and mills, among others. Despite their robustness, induction motors can exhibit both electrical and mechanical faults, with mixed eccentricity being a mechanical rotor fault that can occur due to errors in the manufacturing process or incorrect bearing mounting, which in turn can cause adverse consequences for the machine. In recent years, various methodologies have been studied and presented for fault diagnosis in induction motors, although many of them present certain limitations, as in some cases, some phenomena may mask the fault signals, and even in some cases, the load may affect the fault-related frequency amplitudes, thus leading to false diagnoses. This paper analyzes the effect of the load on the amplitude of the rotor rotational frequency components of the current signals of an induction motor operating under mixed eccentricity fault by applying the Fast Fourier Transform and examining the frequency components at four different load levels.

This study introduces an analytical tool for predicting the lifespan of bearings, combining machine learning and higher-order spectral analysis. The purpose of this approach is the use of acceleration as the primary parameter, capturing the essence of the bearing's functionality and its diverse states through both horizontal and vertical components. Specifically, this research delves into frequency analysis methods, centering on the Bispectrum of the acceleration signal. This technique offers an insight into the complex spectral relationships among frequencies throughout the bearing's life, by exploring the phase relationships between frequency components and unveiling the nonlinear interactions within the signal. This methodology is particularly valuable for bearing lifespan prediction, as it uncovers intricate patterns that traditional analysis might overlook. The aim is to not only elevate the precision of lifespan predictions but also to deepen the understanding of the failure mechanisms at play, thereby enhancing predictive maintenance strategies and industrial efficiency.

The monitoring of vibration signals acquired in non-rotating parts represents a widespread methodology for induction machine health assessment. These signals provide direct insights about mechanical and electromagnetic signatures. However, detecting mechanical defects such as shaft misalignment remains challenging due to the complex interactions between mechanical and electromagnetic domains. The accurate measurement and acquisition of vibration signals plays a key role on elucidating possible fault indicators, which stem from different multi-physical mechanisms. The present paper presents an experimental study to analyze the effect of sensor location and placement within an induction machine housing for accurate mechanical fault identification. The comparison is performed for classical time and frequency-domain fault indicators. In addition, different time-frequency evolutions of start-up transient signals are introduced and compared.

DC electrical motors and generators continue to play an important role in many industrial applications despite their declining use. A poor maintenance may yield significant economic losses for the industries where they operate. One of the common symptoms of failure in these machines is the presence of sparks in the interface between brushes and commutator. This phenomenon is usually linked with a diversity of anomalies and defects which may even lead to the forced motor outage. Therefore, its detection in its incipient stage and the quantification of its severity becomes crucial to adopt proper maintenance actions in due time to prevent the corresponding fault. This paper explores an innovative method to detect and assess the sparking phenomenon in DC machines. The method is based on the analysis of steady-state and transient stray flux signals and the further identification of low and high frequency harmonics linked with the fault. The results prove the high potential of the approach for becoming a reliable tool for the assessment of the commutation quality in DC machines.

Condition monitoring and predictive maintenance of induction motors have great relevance in industrial applications. Nowadays, there are different techniques to analyze electronic signals from different types of sensors. At the same time, the application of FPGA-based hardware acceleration has gained traction in recent years due to the increasing demand for time and energy optimization. However, the application of hardware-accelerated algorithms in condition monitoring applications has yet to be explored despite its potential to improve online fault detection systems. This paper presents the implementation of an FPGA-based hardware-accelerated thermal image processing pipeline on the PYNQ Z2 board based on two image statistical features (mean and standard deviation) to detect five different induction motor mechanical fault conditions: misalignment, unbalanced load, bearing defect on the outer race, two broken rotor bars, and a healthy case that was used as reference. The hardware implementation of the thermal image statistical feature computation made it possible to reduce the computational load and the computation time on the development board.

Stray flux analysis has proven an effective method for electrical machine, and especially induction motor, diagnosis due to its fully non-invasive, affordable approach. State-of-the-art has successfully combatted the most important shortcomings via signal processing and sensor spatial arrangement techniques. The resulting time-frequency domain spectrogram is an information rich, ideal candidate for feature extraction. This work proposes a complete, adaptable stray flux analysis setup for collection of the most common faults' pertinent information with respect to machine learning requirements, compliant with perceived industrial limitations. A representative experimental setup is utilized as proof of concept.

Stator faults are one of the most common types of failure in induction machines. This type of motors are widely used in industry and predictive maintenance becomes crucial. Hence, this paper presents a methodology for the detection of stator asymmetries that enhances the identification of fault components and its severity. The method is based on the capture of current signals and on the subsequent application of a time-frequency transform, in this case, the Short Time Fourier Transform (STFT). The results shows clear signatures linked the fault that appear in the time-frequency map. Moreover, the intensities of these signatures increases as the fault gets worst, so different fault indicators are introduced for determining the fault severity level. The results of this work, obtained for different levels of load, allow to detect the presence of stator asymmetries and its severity

This paper shows the preliminary results of a proposed methodology for outer race fault diagnosis of contaminated rolling bearings. The proposed method is based on a low-cost thermographic sensor. This sensor allows for the acquisition of thermographic images, which are subsequently processed by means of statistical and non-statistical indicators. The obtained results shows a correct fault classification of 4 condition states as follows: healthy rolling bearing, outer race fault + 1 g of magnetite-contaminated bearing, outer race fault + 2 g of magnetite-contaminated bearing, and outer race fault + 3 g of magnetite-contaminated bearing

Induction motors play a fundamental role in most industrial processes due to their high efficiency and robust performance. For this reason the condition monitoring of these equipment is of great relevance. One of the most studied motor components is the bearing. Bearings reduce friction in the rotor allowing it to move freely, being a key factor in the correct operation of the induction motor. The most common bearing failures occur in the outer race. In this work we present the development of a system based on thermographic image processing to identify gradual failures in the outer bearing race of an induction motor. The bearing failure cases were induced by drilling holes in the outer race of metallic bearings with ascending diameters of 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm. The experiments were carried out in a kinematic chain integrated by an induction motor and load elements. Thermographic images were acquired with a low-cost infrared sensor, and then segmented into three zones: motor rotor, motor body, motor backside. Subsequently, fifteen statistical parameters were calculated and processed by principal component analysis. The system obtained two characteristic features capable of effectively differentiating the bearing failures.

Within the manufacturing industry, the condition of cutting tools directly impacts the quality and costs of machining, which has driven the development of various methodologies to identify and monitor tool wear. In recent years, the stray magnetic flux has started to be used as a physical variable for wear detection, giving good results both individually and in combination with other physical quantities. The present research proposes the analysis of stray magnetic flux signals in a CNC lathe in conjunction with image analysis of the machined surfaces and the cutting tools in order to detect tool flank wear with cutting speed variation in the machining of 6061 aluminum. The results report the correct detection of the different levels of wear, demonstrating the ability of each of the methodologies to detect the level of wear regardless of the cutting speed used for machining, as well as the future improvement with the fusion of the techniques to obtain more reliable results for a robust detection system.

Bearing failures are among the most common faults in induction motors. In industry, their detection is usually carried out through the analysis of vibration data. However, there are applications in which this technique cannot be used. In these cases, the use of alternative quantities is primordial to reach a reliable conclusion of the bearing condition. The analysis of current and stray-flux signals has been proposed as alternative way to diagnose the bearing condition; despite it has been proven the complexity of using these signals for reaching an accurate conclusion about the condition of the bearings, it has been also demonstrated their potential for obtaining very useful information for the diagnosis of these elements. This work proposes the computation of statistical indicators obtained from stray-flux signals to detect the presence of corrosion in bearings. It is proven the usefulness of some of these indicators not only to detect this fault but also to discriminate versus other failures. In the work it is also shown the superiority of using this approach with stray-flux signals compared to other electrical quantities with regards to the detection of the considered fault.

Magnetic flux analysis is a condition monitoring technique that is drawing the interest of many researchers and motor manufacturers. The great enhancements and reduction in the costs and dimensions of the required sensors, the development of advanced signal processing techniques that are suitable for flux data analysis, along with other inherent advantages provided by this technology, are relevant aspects that have allowed the proliferation of flux-based techniques. This article reviews the most recent scientific contributions related to the development and application of flux-based methods for the monitoring of rotating electric machines. Particularly, aspects related to the main sensors used to acquire magnetic flux signals as well as the leading signal processing and classification techniques are commented on. The discussion is focused on the diagnosis of different types of faults in the most common rotating electric machines used in industry, namely: squirrel cage induction machines, wound rotor induction machines, permanent magnet machines, and wound field synchronous machines. A critical insight of the techniques developed in the area is provided and several open challenges are also discussed.

Tool condition monitoring (TCM) is one of the most relevant tasks during a machining process. The latest high-quality productivity standards make it essential to monitor the cutting tool wearing. Current TCM methodologies demand the installation of sensors near the working area, which in practical terms, it is not the most optimal solution since the final diagnosis can be disturbed by noisy signals and direct interferences with the machining process. This article proposes a novel noninvasive methodology based on the time–frequency analysis of the stray flux captured around the spindle-motor to detect and estimate the wearing level in cutting tools. Moreover, a new fault indicator based on this quantity is introduced through the application of the discrete wavelet transform. The results obtained are promising and demonstrates the effectiveness of the proposal to become a complementary source of information to classical approaches. This is validated with a Fanuc Oi mate computer numeric control turning machine for three different cutting tool wearing levels and different cutting depths.

Kinematic chains have a fundamental role in the modern industry thanks to the great variety of applications where they can be found. For this reason the development of new fault detection methods has gained traction in recent years. Different physical signals have been used for the diagnosis of kinematic chains such as electric current, mechanical vibrations, or stray flux; nonetheless, the analysis of temperature signals measured by thermographic cameras has proven to be an effective way to detect certain types of failures complementing the existing work on this area. This paper presents the development of an infrared image processing system for the detection of electromechanical faults (misalignment, unbalance, broken bars, bearing defects, and gearbox wear) on a kinematic chain composed by an induction motor, an output pulley, a plastic transmission band, and an alternator. The system is based on the automatic segmentation of a region of interest associated to the kinematic chain through the implementation of image processing techniques and the calculation of statistical characteristics from the histogram of a thermal image acquired by a low-cost thermographic camera.

Electric motors condition monitoring courses, due to their eminently practical nature, have been seriously affected by the irruption of the COVID-19 pandemic. Classical laboratory sessions, face-to-face demonstrations and seminars and student visits to real industrial facilities have had to be replaced by other learning mechanisms adapted to the new context of virtual teaching. Instructors of these courses have been forced to devise, often in a very short time, suitable strategies to replace the aforementioned presential mechanisms, by substituting them by solutions adapted to the virtual learning that, on the one hand, guarantee the acquisition of the necessary competencies and skills, and, on the other hand, maintain the student interest and motivation. This paper explains the strategies adopted in the context of two courses related to electric motors condition monitoring that are taught at the authors’ University. The described strategies have not only shown satisfactory results to transmit the regulated contents but also open new perspectives to enhance the future teaching in those courses. The ideas presented here may be useful for other instructors teaching similar courses or other subjects related to the considered area.

Despite the use of brush dc motors in industry has been decremental during recent decades, they are still employed in many industrial sites, even in high output power applications. However, the limited number of research works addressed to them yields a lack of predictive maintenance techniques to diagnose relevant faults in these machines, which may have catastrophic repercussions. Sparking in the commutator/brushes system is a symptom of incorrect operation linked to different possible failures (brush wear, deficient contacts, commutator defects...) and its detection can be crucial to prevent a further development of these anomalies. This work proposes a method for sparking assessment in dc motors based on the computation of the energy of specific signals resulting from the DWT analysis of the armature current. The experimental results confirm that the increment of the sparking activity yields low frequency components that provoke an increment in the energies of high order wavelet signals. Based on this fact, a new indicator of the sparking activity is proposed that shows a high effectivity according to the obtained results. The developed indicator can be a valuable tool for field engineers, satisfying their need of an online, simple and reliable method to assess the condition of these critical parts of dc motors.

The widespread use of induction motors in industry has led to an increase in the importance of predictive maintenance to avoid untimely shutdowns. On the other hand, their extensive utilization has yielded the proliferation of soft starters that are employed to avoid high currents at start-up. Fault diagnosis techniques such as stray-flux analysis are of interest to diagnose and prevent motor failures. In this regard, this paper studies the validity of the stray-flux-based techniques in motors started via soft-starters. More specifically, it is proposed the analysis of the electromotive forces induced under the starting by the stray-flux in external sensors attached to the motor frame. Various levels of failure at different sensor positions are studied. Finally, a fault severity indicator is introduced based on components energies that appear, and different fault levels are compared. The results enable us to prove the potential of the stray-flux based technique for the rotor condition monitoring of soft-started motors.

Stray flux analysis is an interesting source of information for the diagnosis of Induction Motors (IMs). The widespread use of these motors in industry leads to a necessity of additional tools and methods for their predictive maintenance. On the other hand, soft-starters are increasingly used to reduce the high consumption of IMs at start-up. In this work, AI techniques based on convolutional neural networks are applied to detect rotor faults in soft-started motors. The objective is the automatic early detection of broken bars, avoiding the necessity of user intervention to interpret the obtained results. This work proves the potential of the methodology, including a successful set of experimental results.

In this work, the application of multifractal spectrum of higher order cumulants slice and bicoherence of stator current signals is proposed as a way to detect field winding faults in wound field synchronous motors. These signals are analyzed both under starting and under steady-state regimes. Likewise, a quantitative indicator based on the summation of the first three log cumulants of the scaling exponents obtained from the multifractal analysis is proposed. In addition, a comparative study is carried out during starting and at steady-state, obtaining satisfactory results that prove the potential of the proposed methodology for its implementation in real applications.

Infrared thermography can be a very useful technique for condition monitoring because the most common faults suffered by induction motors cause a temperature rise in the motor's frame. Moreover, this technique is non-intrusive, affordable and very sensitive due to the substantial technical progress in the design and development of new thermal cameras. However, data interpretation and decision making from the resulting infrared images is one of the major limitations of this technique, because it is directly dependent on the operator's experience. Several automated expert systems have been developed using machine learning and, to a lesser extent, with deep learning algorithms. The objective of this paper is to develop a diagnosis tool, based on infrared imaging and deep learning algorithms, applicable to induction motors working in transient conditions. The developed classifier, after training, presents high accuracy levels, classifying the images into one of the five considered scenarios and even at the early stages of the transient state. This methodology can be applied in a broad variety of scenarios with substantial cost saving and offering high-safety standards.

During a machining process, it is very important to carry out the corresponding tasks using cutting tools in a healthy condition, since the quality of the manufactured component can be seriously deteriorated when using worn tools, leading to a possible increase of production costs. Current methodologies are capable of monitoring and diagnosing the wear on the cutting tool adequately. However, in most of them the use of sensors located near the working area is necessary and indispensable, which in practical terms is not the most optimal solution since it is desirable to rely on non-invasive techniques. In this work, the impact of the use of worn cutting tools on the stray flux captured around the induction motor (IM) driving the spindle chuck is studied using time-frequency decomposition mathematical tools (and, more specifically, the short-time Fourier transform (STFT)). The experimentally obtained results show the appearance of some specific frequency components having considerable amplitudes for worn cutting tools, and also demonstrates the potential that this technique has to provide highly relevant information to determine the presence of worn cutting tools. Therefore, it becomes an excellent informational source for the diagnosis of these types of failures, taking advantage of its non-invasive nature.

The analysis of the external magnetic field has been proven to be an effective way to diagnose different types of faults in induction motors. Classical methods based on permanent regime are being complemented by recent approaches that rely on the analysis of the magnetic field during transient operation of the machine. Most of these approaches have been applied with success to detect failures in cage induction motors, but few works have dealt with their wound rotor counterparts. This paper proposes the analysis of the magnetic field in the vicinity of the machine to detect rotor failures in wound rotor induction motors. Classical methods based on the Fast Fourier transform of steady-state signals are compared with recent methods relying on the analysis of signals captured under starting. Different positions for the considered flux sensors are considered and novel indicators are presented for the determination of the level of fault severity.

The possible consequences of the presence of different faults and defects in induction motors in terms of their availability for the production process are well-known. These failures may lead to catastrophic effects, causing unexpected production downtimes and costly repairs, among many other negative effects. Nonetheless, another less perceived consequence of the existence of these failures relies on their repercussion in terms of efficiency reduction of the considered motor. It is easily understandable that the presence of different faults or defects causes a drop in the motor efficiency, but very few works have deepened in the computation of the actual efficiency decrements caused by different types of damages. In a very recent contribution, different tests were performed to calculate the efficiency reductions caused by different types of rotor damages, as well as by bearing failures, obtaining very interesting results on the impact of these failures over the motor performance. The present work is intended to deep in this area, analyzing additional mechanical and electrical faults. Different types of misalignments between motor and load, loosened bolts, cooling system problems and insulation degradation are studied, and the corresponding efficiency curves for different load levels are calculated in each case and compared with the healthy case. The results ratify the significant efficiency impact of most of these failures and emphasize the importance of a proper maintenance of the motor for enabling its optimum performance.

This work presents an exact analytical equation for the calculation of the switch-on current of induction motors considering the general case of unequal stator and rotor parameters. Based on this analytical solution of the differential equations, the influence of the motor parameters on the amplitude and the duration of the electrical transient is investigated. The exact knowledge of the transient current is necessary for the assessment of its potential impact on transient motor current signature analysis methods for fault diagnosis.

Induction motors are fundamental elements in the industry field since they perform several tasks under a broad variety of conditions, a situation that affects its performance making them susceptible to fail despite its robustness. Over several decades, many techniques and methodologies to assess the healthiness state of electrical motors have been proposed and implemented successfully. In this regard new advances in the signal processing field has taken a great importance since these emerging tools are used to accomplish fault diagnosis tasks in order to increase the reliability of such processes. So, it is very important to explore the use of new signal processing techniques to detect and diagnose in a timely manner faults in electrical motors. In this work, it is proposed to use the wavelet entropy of the stray flux signal captured by a coil sensor to estimate the winding insulation status of induction motors, a very common failure presented on this type of drives, that if not attended on time, it can end in a catastrophic and irreversible fault in a matter of minutes. The proposal uses suitable time-frequency decomposition (TFD) tools to isolate the studied fault. The results obtained show that the methodology proposed here can be considered as an excellent alternative for estimating the winding insulation healthiness state of an induction motor online and efficiently, as well as being possible to implement it in a programmable logic device.

Infrared thermography (IRT) is a noninvasive technique that is drawing an increasing attention in industry. The spectacular advancement in the features of the infrared cameras that has come together with their progressive cost reduction has expanded the use of this technique to many industrial applications that were unfeasible just a few years ago. This paper compiles and comments the most recent scientific contributions related to the application of this technique in the industrial context. The paper classifies the analyzed references into three main groups: electrical, mechanical, and other applications. Especial emphasis is made on induction-motor-related applications of the IRT due to the extensive participation of these machines in the industrial context. The paper provides a critical review of most of the analyzed references, emphasizes the way in which the infrared technique is applied to the specific application and presents the limitations and pending issues as well as future challenges regarding the application of the technique.

In recent decades, the use of infrared thermography (IRT) has proliferated in a wide diversity of industrial applications. The spectacular enhancement of the infrared data acquisition equipment together with the significant decrement in their cost has enabled the utilization of this technology in applications and processes where it was not even considered years ago. This Special Section was conceived to attract recent investigations proposing the use of IRT in different cases concerning the monitoring and diagnosis in industrial machines, materials, and facilities. The 15 selected papers are illustrative of the dynamic activity in this research area, as well as the diversity of new applications in which the technology can be employed as the main detection tool or in some applications as a complementary tool of diagnosis.

Predictive maintenance of electric motors is a hot topic nowadays. Due to the extensive participation of these machines in industry, there is an increasing demand of graduate engineering students with skills in this area. In spite of this fact, in many Universities, engineering programs did not include specific courses in this specific area. In recent years, there has been a certain increase in the offer of courses devoted to electric motors condition monitoring. Due to the constant research dynamism of this area, these courses should be prepared to incorporate the latest technological advances in order to instruct the students on the use of the most recent technologies. This paper presents several laboratory sessions that have been built in the context of a course dealing with the maintenance of electric motors. The sessions are designed to facilitate the student participation, promoting the collaborative work and facilitating the use of information and communication technologies (ICTs). At the same time, they are conceived to instruct the students with the most recent techniques for electric motors condition monitoring.

Electric motors condition monitoring is an area that is living a continuous dynamism. There is a strong effort in the search of new, reliable techniques that are able to detect different types of failures and that overcome the drawbacks of the currently available methods. In this regard, vibration and current data analysis are well known techniques that have shown satisfactory results for the detection of certain failures. However, these techniques do not avoid some problems, such as their occasional false indications, which can lead to catastrophic consequences for the involved industries. For instance, this happens when diagnosing rotor faults via current analysis, where there are certain situations that can lead to either false positives or negative indications, such as the presence of oscillating load torques or the existence of non-adjacent bar breakages. Recently, the analysis of the magnetic field in the vicinity of the motor has been proposed as a promising tool to overcome some problems of the classical approaches. However, few works have been focused on the analysis of steady-state signals. This paper explores the analysis of external magnetic fields during transient operation as way to enhance the reliability when detecting rotor faults. The results are promising and show the high potential of this technique to become a complementary information source in cases where the classical tools are not conclusive.

Infrared thermography is emerging as a very interesting complementary technology for the condition monitoring of industrial electric motors. The expertise acquired from the infrared inspections that have been carried out over years in electric installations and static machines has been recently applied to electric motors, proving the potential of this technique to detect several types of common anomalies in these machines. Recent works have shown that infrared thermography provides very useful information for the diagnosis of cooling system problems, deficient bearing lubrication, transmission system problems or stator winding asymmetries, among others. The progressive incorporation of the infrared technology in electric motors predictive maintenance programs comes together with the necessity of new methods that can make it possible to detect the anomalies in the infrared images or, at least, that make the interpretation of these images easier. In this context, this work presents a representation method based on the infrared images which relies on the study of the isotherms. The method makes it easier to display the temperature gradient, as well as to locate the source of the anomaly. The methodology referred to in this paper is used to detect several types of faults in field motors operating in a petrochemical plant and the results confirm the potential of the proposed method.

This paper presents the use of a novel method dealing with mathematical morphology to de-noise spectral contents. This approach is based on two fundamental operators which are dilation and erosion. This type of filter does not need a huge computing time because the operations involved are simple. The goal of this filter in the application considered in this paper is to de-noise the spectrum of the stator current demanded by an induction motor in order to facilitate the self-extraction of occasional fault components. Furthermore, the induction motors are widely used under speed variation conditions, usually fed by an inverter. Experiments showed in this paper are convincing and suggest the possible extension of the method to a wide range of applications.

Infrared thermography is a technique that has been extensively used for the periodic inspection of electrical installations, distribution lines and power transformers. Its utilization for the condition monitoring of electric motors is much more recent but it has proven to be very effective for the detection of some failures such as cooling system problems, deficient bearing lubrication, problems in the transmission system or even stator asymmetries. In this context, the use of the infrared thermography technique may bring interesting advantages and constitutes an excellent information source to complement the diagnostic provided by other well-known quantities, such as currents or vibrations. As a consequence, it is a must to train future engineers in the use of this tool since it is expected that it will be increasingly employed in industry. In this regard, the development of suitable tools relying on this technique is an interesting option that enables a twofold objective: on the one hand, they can be used in industry for the diagnostic of certain failures in an automatic way and, on the other hand, they can be employed as educational tools for introducing the students on the use of this technique. This paper develops a processing tool based on infrared thermography that relies on a particular representation that is derived from the obtained infrared images and that makes it possible to achieve the aforementioned two objectives.

With the expansion of the use of electrical drive systems to more critical applications, the issue of reliability and fault mitigation and condition-based maintenance have consequently taken an increasing importance: it has become a crucial one that cannot be neglected or dealt with in an ad-hoc way. As a result research activity has increased in this area, and new methods are used, some based on a continuation and improvement of previous accomplishments, while others are applying theory and techniques in related areas. This Special Section of the IEEE Transactions on Industrial Informatics attracted a number of papers dealing with Advanced Signal and Image Processing Techniques for Electric Machine and Drives Fault Diagnosis and Prognosis. This editorial aims to put these contributions in context, and highlight the new ideas and directions therein.

In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three “problem transformation” methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation.

The use of soft-starters has proliferated in industrial induction motors to damp the negative effects of high-starting currents, among other reasons. Despite the fact that soft-starters reduce the probability of rotor damage, some industrial cases of rotor failures in soft-started motors have been reported. Over recent years, a novel diagnosis trend based on the analysis of the motor startup current is rapidly drawing the attention of the industrial maintenance community due to the important advantages of that method versus other well-known approaches. An interesting variant of that trend relies on the study of some specific wavelet signals resulting from the Discrete Wavelet Transform (DWT) of that current and on the subsequent computation of fault severity indicators. This method was applied with success to the rotor assessment of motors started direct-online and even of certain soft-started induction motors. However, the massive validation of the method in different soft-starter models and with different topologies of their power block was still pending. This issue is solved in the present work which makes use of extensive testing to obtain a huge set of startup signals corresponding to a motor that is started with four different soft-starter variants. The results prove that, despite the identification of the fault components is more difficult when using these drives, it is clearly possible to separate the healthy and faulty condition regardless of the model used.

Mechanical faults amount to a significant occurrence rate in industrial induction motors. Typically, vibration data analysis has been employed for the detection of this type of failures. However, this technique has some drawbacks (need of sensor installation, difficult discrimination between certain faults, etc...) that may make the application of follow-up diagnostic techniques advisable. In this context, the analyses of currents or temperature data have revealed to be very interesting informational sources for obtaining a more reliable diagnosis of this type of faults. This work presents several case studies that illustrate the application of infrared thermography to detect mechanical faults in induction motors; all these cases are referred to field motors operating in a petrochemical plant and prove the potential of the technique to detect several types of mechanical failures (bearing faults, misalignments, belt transmission problems, etc...) even in some cases in which vibration data analysis was not conclusive enough to indicate the presence of the problem.

Industrial machinery makes extensive use of induction motors as primary motion supplies for the associated kinematic chain. These motors and the kinematic chain are susceptible to failures in one or several of the components making the detection of the faults a major issue for industries. Thermography is a technique that has been used for monitoring and diagnosis in industrial facilities and it is suitable for the monitoring of induction motors and the associated kinematic chain. This technique is an aid for the detection of faults and the diagnosis of the operating condition of industrial machinery. Several research works have used thermography for this purpose, but the problem is the manual adjustment that needs to be done to the thermal camera in order to obtain thermal images, named thermograms, that give the true temperature readings of the objects in focus. This paper presents a novel methodology that makes the adjustment of the thermal camera in an automated way, using additional external temperature sensors to calibrate the thermal images provided by the low-cost thermal camera to give readings of the true temperature of the objects. Experimentation is performed on an induction motor with an associated kinematic chain to test the efficiency of the proposed methodology.

The analysis of the startup current has been recently revealed as an excellent informational source for the diagnosis of the rotor condition in electric motors. In induction machines, the analysis of such current may provide important advantages versus the classical method employed for rotor assessment (Motor Current Signature Analysis, MCSA), as the avoidance of eventual false indications obtained with the traditional approach or its suitability for any operating condition of the machine. In this context, the startup current analysis is increasingly employed to complement (or even to discard) the diagnostic conclusions obtained with MCSA. With regards to synchronous motors, the analysis of the startup current is also an interesting option to determine the condition of the damper cage. However, the investigation on this latter issue is still incipient and only circumscribed to particular geometries of the damper. The present paper analyses the suitability of the startup current analysis for assessing the condition of the damper cage in salient-pole synchronous motors. The interesting results show that, for these machines, due to the particular construction of the damper, false positive indications are obtained when relying on startup analysis. This important fact is of great interest to avoid false diagnostics in the field when assessing the condition of such elements.

In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.

Advanced analysis of the motor starting currents enables very reliable diagnostics of the rotor condition, providing immunity against the eventual false alarms obtained with the conventional MCSA. However, the application of this methodology has been mainly restricted to the case of line-started machines. Its extension to the cases of soft-started or inverted-fed motors has been barely investigated, especially because the probability of rotor damage is claimed to be reduced under such conditions. Nonetheless, industrial cases related to rotor damages in induction motors operated via soft-starters and frequency-converters have been recently reported. This paper proposes a twofold transient-based approach for the reliable assessment of the rotor condition in induction motors operated with such elements. The method relies on the advanced analysis of the motor starting current by combining two types of time-frequency approaches (based on discrete and continuous transforms) in two subsequent stages: 1) fault pattern identification and 2) fault severity quantification. Field results obtained with large motors operating in a sewage treatment plant confirm the validity of the methodology for the rotor assessment in induction motors operated both via soft-starters and frequency converters.

It has been reported in the past that, the non-adjacent broken rotor bars in three-phase induction motors can influence the diagnostic ability of the line current frequency spectrum. In this paper, the traditional line and zero-sequence current broken rotor bar fault signatures are studied for different broken rotor bar fault configurations. More specifically, the cases to be investigated are healthy, one broken bar, two adjacent broken bars and two non-adjacent broken bars. The results shown in this paper lead to the conclusion that the zero-sequence current presents several advantages compared to the traditional line current-based method. This can be especially useful in industrial motors where there is the possibility of measuring these currents since it can help to avoid some false negative cases obtained with the conventional Motor Current Signature Analysis.

This paper presents an initial study to provide further insight on the conditions under which a natural bar breakage was reproduced experimentally. With this aim several magnitudes at a rotor bar's narrowing (hot spot) in an induction motor during heavy transients: long startup and plug stopping, are calculated in order to compare it with the experimental data, which suggested that the bar breakage process can be modelled by thermal fatigue. A combined analytical electrical and thermal procedure is used to obtain the current and temperature distribution in the cage based on values previously computed by 2D electromagnetic FEM, whilst the temperature, resistance and final stresses at the defect are calculated by Finite Element Analysis. The results show that the asymmetric heating alters the current distribution in the cage throughout the transients and that the interbar currents play a fundamental role in the evolution of the defect.

The promotion of the students' transversal or generic competencies is one of the key points of the new European educational framework. Today, companies hiring graduate students are not only concerned about their technical background but also transversal skills and competencies. Hence, it becomes necessary to adopt measures to promote and cultivate these competencies among students. The students' involvement in innovative research projects brings an excellent opportunity to achieve this goal: competence dimensions as team work, effective oral and written communication, effective use of informational resources, independent learning, foreign language, problem resolution among many other can be effectively developed by involving students in multidisciplinary teams conceived to develop this type of projects. This paper focuses on this idea, remarking the usefulness of these projects to achieve the goals pursues by the new educational framework in the context of generic competencies. A real success story at the Universitat Politecnica de Valencia, based on a project relying on the development of an intelligent hardware system (SEE4ME) is presented. It confirms the ability of this approach to promote generic skills and competencies among students.

Implementation of unsupervised induction motor condition monitoring systems has drawn an increasing attention recently among motor drives manufacturers. In the case of soft-starters the possibility of incorporating fault detection features to their conventional functions provides an added value to those elements. Design and development of advanced algorithms that are able to automatically detect and alert about possible failures without requiring continuous human inspection is a challenging research goal. In this paper, an algorithm for the automatic detection of rotor damages in induction motors in the case of soft starting is proposed. The twofold approach relies, first, on the application of a time-frequency transform to the starting current signal and, second, on a pattern recognition stage based on the treatment of the time-frequency representation as a symbolic sequence. The innovation of this work is the implementation of the proposed approach for the automatic detection of rotor cage faults in soft-started motors. The experimental results prove the usefulness of the approach for the automatic detection of such faults and its potential for possible future implementation in soft-started machines.

The present paper presents an study on the harmonics introduced by thyristor-based soft-starters in the induction motor starting current. It is observed how the use of these starting elements leads to the amplification of the amplitudes of some specific harmonics. The application of continuous time-frequency decomposition tools enables to visualize the full transient evolutions of these harmonics and discern those that are amplified when using such drives. The identification of the current harmonics introduced or amplified by soft-starters is very useful when applying induction motor fault diagnosis techniques based on the analysis of the startup current, since it enables to discriminate between the components amplified by the soft-starters and those amplified by the fault. Soft-started laboratory motors are employed in this work to study the harmonics amplified by a thyristor-based soft-starter under non-ideal supply conditions. The results reveal that these signal processing tools may be very useful to analyze the components introduced by the drive and, at the time, discriminate them versus eventual fault-related harmonics.

This paper proposes a new method to study the time-frequency diagrams used to diagnose induction motors. The complex pattern of evolving harmonics and interferences obtained from the analysis of the stator current during a high-inertia direct-on-line startup, such as the one needed for an acceleration test, is analyzed by a non-Markovian Particle Filtering state estimation approach. The introduction of a biasing in the system equations, based on the regularity with respect to previous states, and a prediction stage yields an automation procedure that, with reduced computational needs, allows collecting the most important information present in the diagram for diagnosis purposes: the energy content of significant rotor related harmonics during the transient, as well as the torque developed by the machine. An initial validation of the approach is carried out for two types of motors under different fault conditions.

This paper discusses the effect of the operating load as well as the suitability of combined startup and steady-state analysis for the detection of bearing faults in induction machines, Motor Current Signature Analysis and Linear Discriminant Analysis are used to detect and estimate the severity of an outer race bearing fault. The algorithm is based on using the machine stator current signals, instead of the conventional vibration signals, which has the advantages of simplicity and low cost of the necessary equipment. The machine stator current signals are analyzed during steady state and start up using Fast Fourier Transform and Short Time Fourier Transform. For steady state operation, two main changes in the spectrum compared to the healthy case: firstly, new harmonics related to bearing faults are generated, and secondly, the amplitude of the grid harmonics changes with the degree of the fault. For start up signals, the energy of the current signal frequency within a specific frequency band related to the bearing fault increases with the fault severity. Linear Discriminant Analysis classification is used to detect a bearing fault and estimate its severity for different loads using the amplitude of the grid harmonics as features for the classifier. Experimental data were collected from a 1.1 kW, 400V, 50 Hz induction machine in healthy condition, and two severities of outer race bearing fault at three different load levels: no load, 50% load, and 100% load.

One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.

Photo-voltaic generators are no longer an energy source mainly useful for off-grid applications, such as satellites. Their presence in the energy mix of developed countries has substantially increased. Moreover, their price downward trend, together with the upward trend of electric energy prices, might increase their role, if allowed by the legal context. The behavior of photo-voltaic generators is complex, especially under partial shading. In order to enhance the learning of their characteristic current-voltage curve, the paper presents an easy and fast to apply model. The state of the art for modelling photo-voltaic cells has been reviewed. Upon the different types of models, a balanced option between complexity and precision has been chosen. Then, a model of a panel is built, by connecting an arbitrary number of cells, with an arbitrary number of bypass diodes. An arbitrary number of panels are connected to construct a string, including the correspondent blocking diode, and a set of strings constitute the generator matrix of panels. The model has been implemented in Matlab. Finally, several exercises have been developed, following a "learning by doing" methodology: students learn, from the influence of the cell parameters on its current-voltage curve, to the generator behavior under partial shading.

Advanced analysis of motor currents during transient operation has proven to be a valuable source of information to reach a reliable assessment of the rotor condition. This novel approach may help to overcome some of the drawbacks of the classical Motor Current Signature Analysis (MCSA). One of the advantages of the approach is the robustness obtained due to its twofold perspective: 1) Qualitative identification of fault-related time-frequency patterns + 2) Computation of quantification indicators to determine the severity of the fault. Nonetheless, since the research in this area is still incipient, few transient-based fault severity indicators have been hitherto proposed in the literature. Moreover, most of them have been validated for specific startup conditions (the majority for line-started motors). This paper presents a study about the variation of different wavelet-based fault severity indicators for different types of motor starting methods. An experimental test-bed enabling the development of different types of startups with different rotor fault levels enables to obtain real startup currents that are used for the study. Results show the robustness of the indicators even in situations where the classical MCSA does not lead to correct results.

Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.

Up to now, detection of rotor faults in inverter-fed induction motors has received very little attention. This fault is difficult to be detected, since the fault-related components are too close to the fundamental (the inverter usually operates at low slip). Moreover, classic techniques cannot be applied since steady states are not common. The causes of this type of fault are analyzed in the paper, showing its importance. Particularly, cases of real faults in electric traction are exposed. Then, the paper explores the use of linear time-frequency transforms to detect the time-frequency evolution of the fault-related components. It is shown how the most common linear transforms, such as the Short Time Fourier Transform, do not enable the fault detection. The Chirplet Transform (which has never been used for diagnosing purposes), is proposed to obtain the components evolutions, even if they are too close in the time-frequency plane. The technique is validated through startup tests, in which the presence of the fault is quantified when analyzing the stator current.

Industrial maintenance is a field of rapidly increasing relevance. More specifically, maintenance of electric machines and installations is particularly important, since eventual failures in these elements may lead to significant losses in terms of time and money. Due to these facts, the investment and concern in developing proper maintenance protocols have been gradually growing up over these recent years. As a consequence, there is a need to provide future engineers with a solid background in the electric machines and installations maintenance area. The subject `Maintenance of Electric Machines and Installations' has been designed with this purpose. It is taught within an official master degree in Maintenance Engineering. The paper describes the educational experiences reached during the initial years of the teaching of the subject, emphasizing aspects such as the student profiles, the subject approaches, the design of the syllabus, the methodology and the structure of the laboratory sessions. Moreover, the paper discusses other educational strategies which are being introduced to increase the interest in the subject, such as integration of Information and Communication Technologies (ICT), promotion of the collaborative work, inclusion of the possibility of remote learning or development of new assessment systems.

Most of the research work hitherto carried out in the induction motors fault diagnosis area has been focused on squirrel-cage motors in spite of the fact that wound-rotor motors are typically less robust, having a more delicate maintenance. Over recent years, wound-rotor machines have drawn an increasing attention in the fault diagnosis community due to the advent of wind power technologies for electricity generation and the widely spread use of its generator variant, the Doubly-Fed Induction Generators (DFIGs) in that specific context. Nonetheless, there is still a lack of reliable techniques suited and properly validated in wound-rotor industrial induction motors. This paper proposes an integral methodology to diagnose rotor asymmetries in wound-rotor motors with high reliability. It is based on a twofold approach; the Empirical Mode Decomposition (EMD) method is employed to track the low-frequency fault-related components, while the Wigner-Ville Distribution (WVD) is used for detecting the high-frequency failure harmonics during a startup. Experimental results with real wound-rotor motors demonstrate that the combination of both perspectives enables to correctly diagnose the failure with higher reliability than alternative techniques relying on a unique informational source.

In this paper we propose the use of Intelligent Icons for both automatic assessment and representation of asynchronous machines' condition. The method focuses on the analysis of the start-up current for the isolation of a component that is able to pinpoint faulty signatures. The analysis is based on the application of Empirical Mode Decomposition (EMD) which acts as an adaptive filter during the start up and subsequently on the application of Symbolic Aggregate approXimation (SAX) for the transformation of the extracted component into a symbolic representation. Using this symbolic representation, an automated detection procedure can be developed that discriminates between faulty and normal conditions using an intelligent Icons approach while at the same time the information can be presented to the user in a more intuitive way.

The development of portable devices that make the reliable diagnosis of faults in electric motors possible has become a challenge for many researchers and maintenance enterprises. These machines intervene in a huge amount of processes and applications and their eventual failure may imply important costs in terms of time and money. However, the aforementioned issue remains unsolved because most of the developed fault diagnosis techniques rely on the user expertise, since they are based on a qualitative interpretation of the results. This complicates the implementation of these methodologies in condition monitoring systems or devices. The objective of this paper is to propose an integral methodology that is able to diagnose the presence of rotor bar failures in an automatic way. The proposed algorithm combines the Discrete Wavelet Transform with the scale transform for feature extraction and correlation coefficient for pattern recognition. The algorithm is applied to both small and large motors operating in a wide range of conditions. The results illustrate the validity and generality of the approach for automatic condition monitoring of electric motors.

Induction motor condition monitoring has primarily relied on the analysis of currents during steady-state operation through the Fast Fourier Transform (FFT). Nonetheless, this conventional approach has many constraints, most of them related to the usual operation of most motors under situations differing from a pure stationary regime, or directly under transient conditions. Indeed, these situations are the most common in many industrial processes, a fact that reveals the necessity of developing techniques suited for the analysis of machine quantities during non-stationary operation. A vast work has been developed during these recent years in this area, raising many transient-based techniques being able to diagnose machines under transient conditions which furthermore overcome some of the drawbacks of conventional stationary analysis. Most of these techniques are based on the application of proper signal processing tools, especially adapted to analyze transient signals (time-frequency decomposition (TFD) tools). This paper carries out a review of the most significant techniques sustained on transient-analysis, grouping them in accordance to the nature of the TFD tool used in each case. The review intends to emphasize the most relevant contributions of each work and to serve as a useful reference to all authors involved in the area.

This paper introduces a methodology for diagnosing mixed eccentricity fault in squirrel cage induction machines which work under variable load conditions in generator or motor mode; the method is based on the extraction of the instantaneous frequency of the fault related component of stator currents during speed transients, caused by load changes. It is shown that under these conditions, the instantaneous frequency plot of the main fault component versus the slip is a straight line with a specific slope and offset. In addition and for a given pole pair number, this pattern is not dependent on the machine features, nor the way in which the load changes. Besides, the practical methodology of this technique is introduced for diagnosing mixed eccentricity. The approach is validated by laboratory tests.