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.

The detection of uncharacterized events during electromechanical systems operation represents one of the most critical data challenges dealing with condition-based monitoring under the Industry 4.0 framework. Thus, the detection of novelty conditions and the learning of new patterns are considered as mandatory competencies in modern industrial applications. In this regard, this article proposes a novel multifault detection and identification scheme, based on machine learning, information data-fusion, novelty-detection, and incremental learning. First, statistical time-domain features estimated from multiple physical magnitudes acquired from the electrical motor under inspection are fused under a feature-fusion level scheme. Second, a self-organizing map structure is proposed to construct a data-based model of the available conditions of operation. Third, the incremental learning of the condition-based monitoring scheme is performed adding self-organizing structures and optimizing their projections through a linear discriminant analysis. The performance of the proposed scheme is validated under a complete set of experimental scenarios from two different cases of study, and the results compared with a classical approach.

The demand of electric power supply has increase in the industry due to most of its processes are involved with the use of electrical equipment and machines. Electric power generation due to integration of new energy sources has become an important area of continuous development in which Power Quality (PQ) problems must be faced. In this paper is proposed a condition monitoring strategy based on the estimation of statistical time domain-based features and Linear Discriminant Analysis to identify different PQ disturbances in a cogeneration system. The proposed method is first evaluated with a set of synthetics signals that include different PQ disturbances and then evaluated a real data acquired from a cogeneration system. The final diagnosis outcome is performed by means of a Neural Network. The obtained results shown that the proposed method is suitable for being applied in cogeneration system to identify different PQ disturbances.

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.

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.

This study is focused on the current challenges dealing with electromechanical system monitoring applied in industrial frameworks, that is, the presence of unknown events and the limitation to the nominal healthy condition as starting knowledge. Thus, an industrial machinery condition monitoring methodology based on novelty detection and classification is proposed in this study. The methodology is divided in three main stages. First, a dedicated feature calculation and reduction over each available physical magnitude. Second, an ensemble structure of novelty detection models based on one-class support vector machines to identify not previously considered events. Third, a diagnosis model supported by a feature fusion scheme in order to reach high fault classification capabilities. The effectiveness of the fault detection and identification methodology has been compared with classical single model approach, and verified by experimental results obtained from an electromechanical machine.

A great deal of investigations are being carried out towards the effective implementation of the 4.0 Industry new paradigm. Indeed, most of the machinery involved in industrial processes are intended to be digitalized aiming to obtain enhanced information to be used for an optimized operation of the whole manufacturing process. In this regard, condition monitoring strategies are being also reconsidered to include improved performances and functionalities. Thus, the contribution of this research work lies in the proposal of an incremental learning framework approach applied to the condition monitoring of electromechanical systems. The proposed strategy is divided in three main steps, first, different available physical magnitudes are characterized through the calculation of a set of statistical-time based features. Second, a modelling of the considered conditions is performed by means of self-organizing maps in order to preserve the topology of the data; and finally, a novelty detection is carried out by a comparison among the quantization error value achieved in the data modelling for each of the considered conditions. The effectiveness of the proposed novelty fault identification condition monitoring methodology is proved by means of the evaluation of a complete experimental database acquired during the continuous working conditions of an electromechanical system.

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.

The procedures for condition monitoring of electromechanical systems are undergoing a reformulation, mainly, due to the current thermographic affordability of infrared cameras to be incorporated in industrial applications. However, high-performing multifault data-driven methodologies must be investigated in order to infer reliable condition information from the thermal distribution of not only electrical motors but also of shafts and couplings. To address this issue, a novel thermography-based methodology is proposed. First, the infrared capture is processed to obtain a thermographic residual image of the kinematic chain. Second, the thermal distribution of the image's regions of interest is characterized by means of statistical features. Finally, a distributed self-organizing map structure is used to model the nominal thermal distribution to subsequently perform a fault detection and identification. The method provides a reliability quantification of the resulting condition assessment in order to avoid misclassifications and identify the actual fault root-causes. The performance and the effectiveness of the proposed methodology is validated experimentally and compared with the classical maximum temperature gradient procedure.

Power quality (PQ) and PQ disturbances (PQD) are relevant for the industry due to the implied costs in most industrial processes. Besides, it is necessary to maintain the quality standards of the electrical grid to avoid damages in the equipment that is connected to the grid. Due to the nature and characteristics of the PQD present in the voltage and current signals, several studies have focused on detecting and classifying particular disturbances, or simple combinations between two or three of them, without presenting a methodology that describes all of them automatically. Hence, this paper proposes a hybrid approach integrating genetic algorithms (GA) and particle swarm optimization (PSO) with other techniques that make use of their individual capabilities to automatically find a wide range of PQD present in a voltage or current signal, regardless of their nature. To achieve this hybrid approach parameterization, a full PQD model is adopted to automate the search of every one of their parameters. The proposed approach is validated through synthetic signals, real data from the IEEE data base, and through data readings from a real process. A comparison using other recent heuristic techniques is made to show the robustness of the proposed hybrid approach.

Fault detection in induction motors (IM) has been studied during the past decades due to the role that these electric machines play in industry. Regular monitoring is performed on IM to diagnose their operating condition using vibration and stator current analysis. The acquired signal is then processed to extract the characteristic parameters of the fault. The fast Fourier transform (FFT) is used for this task, but it has intrinsic limitations like sensitivity to low signal-to-noise ratio, overlapping of closely-located spectral components, nonstationary signals, and spectral leakage. These limitations have been studied to improve the spectrum estimation, but spectral leakage has not received enough attention, even when its effects can be significant. This paper introduces multirate signal processing techniques that improve the FFT-based methods by reducing spectral leakage with fractional resampling. The methodology is applied to experimental signals to show the improvement of the FFT-based methods for detecting faults in IM.

Induction motors and electric machines in general are important components in most industries. The condition monitoring of these machines is relevant at the industrial facilities for reducing costs and minimizing maintenance downtime. Infrared thermography is a non-invasive technique that allows on-line monitoring of electric machines; characteristic that makes it a suitable solution for industrial environments. However, some external parameters like the camera positioning and location can affect the estimations carried out by the camera. This work presents a methodology for thermal analysis on induction motors, showing how the location of the camera affects the thermography imaging process. Images are taken from different locations, relative to the monitored machine, and a comparative of the results is carried out for an experimental case of study consisting in a healthy induction motor and a motor with a damaged bearing. Results show that despite the fact that a change on the camera location produces a variation on the estimation of the temperature; the difference is not significant for applications on electric machines.

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.

A great deal of effort is being made to increase accuracy and reliability of Condition Based Maintenance systems; for instance, by improved feature selection strategies or optimization approaches of classifier parameters. In this work a novel classification methodology is presented, covering from the characterization of the acquired physical magnitudes to the configuration of the classification algorithms. The proposed methodology provides a more accurate classification structure by identifying and isolating conflictive regions in the classification space and by specialized feature reduction and classification stages for them. The proposed Hierarchical Classification Scheme is composed by sequential layers, in which the clear membership regions are identified first, and the conflictive regions of classification are tackled in upper levels. Such treatment of the conflictive regions is based on new feature space transformation to provide an optimized data understanding and, then, better chances of classification. Improving classification with this method compared to other alternatives implies the avoidance of over-fitting the classification training. Also, the proposed methodology, due to its hierarchical structure nature, offers the opportunity to configure the feature reduction and classification algorithms to obtain the optimal data management.

Gearboxes are important components in industrial applications and its condition monitoring is relevant in industries for reducing costs and minimizing maintenance downtime. Diagnosing wear in gearboxes saves time to prepare appropriate corrective actions, and to ensure that the system does not deteriorate critically. Nowadays, for condition monitoring in gearboxes, vibration analysis is commonly used due to its high reliability. In this work, a reliable methodology for diagnosing different levels of wear in a gearbox through vibration signals, and supported by a theoretical model, is proposed. The theoretical model is based on calculating the characteristic frequencies of the gearbox, with the aim for locating the spectral components of the faults in the vibration signal. Experimentation is done to a healthy gearbox and three wear levels. Results show the reliability of this method that makes it suitable to be used in diagnosing industrial machinery such as in automotive manufacturing applications.

Squirrel-cage induction motors (IM) are the most popular motors used in industry, consuming around 85% of the power in industrial plants. IM are critical components for most industries, and an early detection of induction motor faults has been a main subject of investigation during last years. Broken rotor bars in induction motors are among the major failures that are desirable to detect at early stages because this failure significantly increases power consumption and is responsible for further damage to the machinery. However, the detection of partially-broken rotor bars at an early stage is not so easily achieved. Therefore, it is necessary to use suitable condition monitoring accompanied with signal processing techniques to detect a partially-broken rotor bar. Thus, this paper focuses on a study and evaluation of a condition monitoring method based on the complete ensemble empirical mode decomposition CEEMD of the stator current; aiming to detect partially broken, one, and two broken rotor bars in a line fed EVI during the startup current transient. From the experimental results, the CEEMD is able to graphically show, in the time domain, the physical effect of the fault evolution during the startup current as an abrupt change in the envelope, and finally, these abrupt changes are quantified in a way that the level of fault can be identified correctly. Also, the CEEMD has the advantage of being an online diagnosis method, which does not require knowing a priori the motor current of the healthy condition.

Asymmetrical loads in power systems leads to unbalance voltage/current signals and consequently, adverse effects such as overheating, lifetime, torque, and speed reduction, among others. In order to compensate the level of unbalance and avoid the above mentioned effects, the instantaneous unbalance and symmetrical components estimations have to be carried out. In this work, a methodology for estimating the instantaneous unbalance and symmetrical components through the Hilbert transform (HT) in three-phase voltage and current systems is proposed. First, a bandpass filter is used to remove the harmonics/interharmonics components; afterwards, the HT is applied to obtain the instantaneous amplitude and phase of each voltage/current signal. Finally, the symmetrical components and unbalance percentage are computed. The proposed approach is validated and tested through synthetic signals and under real operating conditions. Besides, a field programmable gate array (FPGA)-based implementation is developed as a low-cost and portable System-on-Chip (SoC) solution.

Nowadays industry pays much attention to prevent failures that may interrupt production with severe consequences in cost, product quality, and safety. The most-analyzed parameters for monitoring dynamic characteristics and ensuring correct functioning of systems are electric current, voltage, and vibrations. System-on-chip (SoC) design is an approach to increase performance and overcome costs during equipment monitoring. This work presents the design and implementation of a low-cost SoC design that utilizes reconfigurable hardware and a customized embedded processor for time-frequency analysis on industrial equipment through short-time Fourier transform and discrete wavelet transform. Three study cases (electric current supply to an induction motor during startup transient, voltage supply to an induction motor through a variable speed drive, and vibration signals from industrial-robot links) show the suitability of the proposed monitoring system for time-frequency analysis of different signals in distinct industrial applications, and early diagnosis and prognosis of abnormalities in monitored systems.

Speed observation of rotating machines under different operational conditions is required in applications from fault detection to traction. Sensorless rotating speed observation has become popular in recent years. The MRAS rotor-flux-oriented and synchronous models are common schemes used for sensorless speed observation. These techniques are affected under low rotating frequencies and variable load conditions. This work presents an experimental analysis on the performance of both schemes during rotating speed estimation under different rotational frequencies and variable-load conditions. Obtained results show performance deficiencies on their estimations according to the rotational speed and load. A double scheme methodology that mixes the MRAS-based and the synchronous speed observers through a compensation function is proposed. Its performance analysis indicates an improvement on the estimated speed at different rotational frequencies with variable load. An FPGA-based implementation is developed as a low-cost, portable system-on-chip solution for real-time online estimation of rotating speed on induction motors.

Machine monitoring is one of the major concerns in modern industry in order to guarantee the overall efficiency during the production process. Several monitoring techniques for machinery failure detection have been developed, being vibration analysis one of the most important techniques. The typical equipment used for vibration analysis is a general purpose single channel spectrum analyzer that most of the cases is not well suited for the specific task and lacks from the capability of simultaneous multiple channel analysis and it is not specifically designed for vibration analysis. The contribution of this work is to present the development of a low-cost FPGA based 3-axis simultaneous vibration analyzer for embedded machinery monitoring with the novelty of a post-processing stage that can be designed and implemented into the same FPGA for automatic on-line detection of specific machinery failures thanks to its reconfigurability. The vibration analyzer has three stages: vibration monitoring with a MEMS accelerometer as sensor, three parallel 1024-point FFT cores and one post-processor for the analysis of the specific vibration related failure, which can be reconfigured to attend the specific task. One of the most important failures in induction motors is the broken bar condition and the developed vibration analyzer was tested to detect this condition on several motors for different failure severities, giving good detection results. Other vibration analysis can be performed by the three channel FFT cores with the reconfiguration of the post-processing unit to detect or enhance a specific characteristic under study

Preventive maintenance is one of the major concerns in modern industry where failure detection on motors increases the useful life cycle on the machinery. Bar breakage is one of the most common failures on motors and their condition monitoring is a mandatory task for industries. Previous works for bar breakage detection are based on off-line current or vibration analysis through the spectrum, but their detectability is compromised under certain operating conditions. The novelty of this work is the proposal of a correlation algorithm that combines current and vibration spectra to enhance detectability where other works fail. Current and vibration monitoring are non-invasive methods and they are preferred for on-line analysis. The proposed correlation between current and vibration allows to detect the broken bar condition on induction motors when no load is applied to the motor, which is the condition where other reported works fail to detect. The contribution of this work is that the proposed algorithm, though complex in computational load, is implemented into a low-cost FPGA (Spartan-3 XC3S1000) that gives a special purpose SOC solution for on-line operation, thanks to the development of a special purpose hardware signal processing unit. The developed FPGA based algorithm was tested for different bar breakage conditions in the induction motors and with several loads, giving as a result a better detectability for the motor failure under conditions where other methodologies fail, with the additional advantage of being a low-cost SOC solution for on-line detection

This paper presents an implementation of a multilayer perceptronneural network and the backpropagation learning algorithm in an FPGA. The resulting implementation, in contrast to others, is a low-cost system with effective resource utilization, capable of training the neural network for any given task. The system is based on a modular scheme conforming to a system-on-a-chip (SoC), where modules can be replaced or scaled to suit a specific application. The system uses fixed-point arithmetic and it was carried out using generic hardware description language. A pipeline architecture is used in order to build a time-efficient system. The efficacy of the systems was tested in a pattern recognition application, tests were done in a low-cost Xilinx Spartan-3E FPGA.