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.

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

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.

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.

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.