A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

[EN] Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.K Barrera-Llanga appreciates the financial support of the Secretary of Higher Education, Science, Technology and Innovation of Ecuador as a personal sponsor entity.Barrera-Llanga, K.; Burriel-Valencia, J.; Sapena-Bano, A.; Martinez-Roman, J. (2023). A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors. Sensors. 23(19):1-20. https://doi.org/10.3390/s23198196120231

SMARTLAB MAGNETIC: A MODERN PARADIGM FOR STUDENT LABORATORIES

[EN] Undergraduate laboratories should provide means to help the student visualize often complex concepts, to achieve a more lasting understanding and, thus, a more significant learning of the ideas presented in the lectures. However, the laboratories often require students to struggle with complex instrumentation operation and tedious data collection and processing that affect negatively their motivation and distract off the learning objectives. This paper introduces through a specific implementation a recent paradigm for student laboratories, designated as SmartLabs, as an effort to help overcome these drawbacks. SmartLabs Magnetic is a combination of existing magnetic circuits test equipment plus a versatile sensors set combined with a standard and affordable data acquisition card and a portable device App. SmartLab Magnetic collects data during magnetic circuits laboratory tests, processes this data to provide results more easily related to the basic concepts being tested and manages the data into reports that can be sent to the student e-mail account within the App. Students' opinions of the relevance, usefulness, and motivational effect of the new SmartLab Magnetic were very positive.Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R. (2016). SMARTLAB MAGNETIC: A MODERN PARADIGM FOR STUDENT LABORATORIES. Sensors & Transducers. 197(2):58-66. http://hdl.handle.net/10251/99063S5866197

A Review of Techniques Used for Induction Machine Fault Modelling

[EN] Over the years, induction machines (IMs) have become key components in industry applications as mechanical power sources (working as motors) as well as electrical power sources (working as generators). Unexpected breakdowns in these components can lead to unscheduled down time and consequently to large economic losses. As breakdown of IMs for failure study is not economically feasible, several IM computer models under faulty conditions have been developed to investigate the characteristics of faulty machines and have allowed reducing the number of destructive tests. This paper provides a review of the available techniques for faulty IMs modelling. These models can be categorised as models based on electrical circuits, on magnetic circuits, models based on numerical methods and the recently proposed in the technical literature hybrid models or models based on finite element method (FEM) analytical techniques. A general description of each type of model is given with its main benefits and drawbacks in terms of accuracy, running times and ability to reproduce a given faultThis work was supported by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i-Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE)Terrón-Santiago, C.; Martinez-Roman, J.; Puche-Panadero, R.; Sapena-Bano, A. (2021). A Review of Techniques Used for Induction Machine Fault Modelling. Sensors. 21(14):4855-4873. https://doi.org/10.3390/s21144855S48554873211

Diagnosis of Rotor Asymmetries Faults in Induction Machines Using the Rectified Stator Current

(c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Fault diagnosis of induction motors through the analysis of the stator current is increasingly being used in maintenance systems, because it is non-invasive and has low requirements of hardware and software. Nevertheless, its industrial application faces some practical limitations. In particular, the detection of fault harmonics that are very close to the fundamental component is challenging, as in large induction motors working at very low slip, because the leakage of the fundamental can hide the fault components until the damage is severe. Several methods have been proposed to alleviate this problem, although all of them increase noticeably the complexity of the diagnostic system. In this paper, a novel method is proposed, based on the analysis of the rectified motor current. It is shown that its spectrum contains the same fault harmonics as the spectrum of the original current signal, but with a much lower frequency, and free from the fundamental component leakage. Besides, the proposed method is very easy to implement, either by software, using the absolute value of the current samples, or by hardware, using a simple rectifier. The proposed approach is presented theoretically and validated experimentally with the detection of a broken bars fault of a large induction motor.This work was supported in part by the Spanish "Ministerio de Ciencia, Innovacion yUniversidades (MCIU)," in part by the "Agencia Estatal de Investigacion (AEI)," and in part by the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i -Retos Investigacion 2018," under Project RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Burriel-Valencia, J. (2020). Diagnosis of Rotor Asymmetries Faults in Induction Machines Using the Rectified Stator Current. IEEE Transactions on Energy Conversion. 35(1):213-221. https://doi.org/10.1109/TEC.2019.2951008S21322135

Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation

[EN] Since it is not efficient to physically study many machine failures, models of faulty induction machines (IMs) have attracted a rising interest. These models must be accurate enough to include fault effects and must be computed with relatively low resources to reproduce different fault scenarios. Moreover, they should run in real time to develop online condition-monitoring (CM) systems. Hybrid finite element method (FEM)-analytical models have been recently proposed for fault diagnosis purposes since they keep good accuracy, which is widely accepted, and they can run in real-time simulators. However, these models still require the full simulation of the FEM model to compute the parameters of the analytical model for each faulty scenario with its corresponding computing needs. To address these drawbacks (large computing power and memory resources requirements) this paper proposes sparse identification techniques in combination with the trigonometric interpolation polynomial for the computation of IM model parameters. The proposed model keeps accuracy similar to a FEM model at a much lower computational effort, which could contribute to the development and to the testing of condition-monitoring systems. This approach has been applied to develop an IM model under static eccentricity conditions, but this may extend to other fault types.This work was supported by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i -Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Terrón-Santiago, C.; Martinez-Roman, J.; Puche-Panadero, R.; Sapena-Bano, A. (2021). Low-Computational-Cost Hybrid FEM-Analytical Induction Machine Model for the Diagnosis of Rotor Eccentricity, Based on Sparse Identification Techniques and Trigonometric Interpolation. Sensors. 21(21):6963-6987. https://doi.org/10.3390/s21216963S69636987212

Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current

(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Fault diagnosis of rotor asymmetries in induction machines working at a very low slip, through Fourier-based methods,usually requires a long acquisition time to achieve a high spectral resolution and a high sampling frequency to reduce aliasing effects. However, this approach generates a huge amount of data, which makes its implementation difficult using embedded devices with small internal memory, such as digital signal processors and field programmable gate arrays or devices with low computing power. In this paper, a new simplified diagnostic signal designated as the reduced envelope of the stator current is introduced to address this problem. The reduced envelope signal is built using only one sample of the current per cycle without any further processing, and it is demonstrated that it carries the same spectral information about the fault as the full-length current signal. Based on this approach, an embedded device has only to store and process a minimal set of samples compared with the raw current signal for a desired resolution. In this paper, the theoretical basis of the proposed method is presented, as well as its experimental validation using two different motors with broken bars: 1) a high-power induction motor working in a factory; and 2) a low-power induction motor mounted in a laboratory test bed.This work was supported by the Spanish "Ministerio de Economia y Competitividad" in the framework of the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad" under Project DPI2014-60881-R. Paper no. TEC-00762-2014.Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R.; Martinez-Roman, J.; Kanovic, Z. (2015). Low-Cost Diagnosis of Rotor Asymmetries in Induction Machines Working at a Very Low Slip Using the Reduced Envelope of the Stator Current. IEEE Transactions on Energy Conversion. 30(4):1409-1419. doi:10.1109/TEC.2015.24452161409141930

Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current's FFT

© 2015 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] The discrete wavelet transform (DWT) has attracted a rising interest in recent years to monitor the condition of rotating electrical machines in transient regime, because it can reveal the time-frequency behavior of the current's components associated to fault conditions. Nevertheless, the implementation of the wavelet transform (WT), especially on embedded or low-power devices, faces practical problems, such as the election of the mother wavelet, the tuning of its parameters, the coordination between the sampling frequency and the levels of the transform, and the construction of the bank of wavelet filters, with highly different bandwidths that constitute the core of the DWT. In this paper, a diagnostic system using the harmonic WT is proposed, which can alleviate these practical problems because it is built using a single fast Fourier transform of one phase's current. The harmonic wavelet was conceived to perform musical analysis, hence its name, and it has spread into many fields, but, to the best of the authors' knowledge, it has not been applied before to perform fault diagnosis of rotating electrical machines in transient regime using the stator current. The simplicity and performance of the proposed approach are assessed by comparison with other types of WTs, and it has been validated with the experimental diagnosis of a 3.15-MW induction motor with broken bars.This work was supported by the Spanish Ministerio de Ciencia e Innovacion through the Programa Nacional de Proyectos de Investigacion Fundamental under Project DPI2011-23740. The Associate Editor coordinating the review process was Dr. Ruqiang Yan.Sapena-Bano, A.; Pineda-Sanchez, M.; Puche-Panadero, R.; Martinez-Roman, J.; Matic, D. (2015). Fault Diagnosis of Rotating Electrical Machines in Transient Regime Using a Single Stator Current's FFT. IEEE Transactions on Instrumentation and Measurement. 64(11):3137-3146. https://doi.org/10.1109/TIM.2015.2444240S31373146641

The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

[EN] This paper introduces a new approach for improving the fault diagnosis in induction motors under time-varying conditions. A significant amount of published approaches in this field rely on representing the stator current in the time-frequency domain, and identifying the characteristic signatures that each type of fault generates in this domain. However, time-frequency transforms produce three-dimensional (3-D) representations, very costly in terms of storage and processing resources. Moreover, the identification and evaluation of the fault components in the time-frequency plane requires a skilled staff or advanced pattern detection algorithms. The proposed methodology solves these problem by transforming the complex 3-D spectrograms supplied by time-frequency tools into simple x-y graphs, similar to conventional Fourier spectra. These graphs display a unique pattern for each type of fault, even under supply or load time-varying conditions, making easy and reliable the diagnostic decision even for nonskilled staff. Moreover, the resulting patterns can be condensed in a very small dataset, reducing greatly the storage or transmission requirements regarding to conventional spectrograms. The proposed method is an extension to nonstationary conditions of the harmonic order tracking approach. It is introduced theoretically and validated experimentally by using the commercial induction motors feed through electronic converters.This work was supported by the Spanish "Ministerio de Economia y Competitividad" in the framework of the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad" (Project reference DPI2014-60881-R). Paper no. TEC-00176-2016.Sapena-Bano, A.; Burriel-Valencia, J.; Pineda-Sanchez, M.; Puche-Panadero, R.; Riera-Guasp, M. (2017). The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions. IEEE Transactions on Energy Conversion. 32(1):244-256. doi:10.1109/TEC.2016.2626008S24425632

The Harmonic Order Tracking Analysis (HOTA) for the Diagnosis of Induction Generators Working Under Steady State Regime

[EN] Improved fault diagnostic techniques in induction generators is a field of growing interest given the negative impact * that unexpected breakdowns have on energy production and on the electrical system. New diagnostic techniques based on induction generator currents monitoring have recently been developed, but their use is still irrelevant despite the advantages that presents to detect electrical faults in the generator. This situation is due to the needs of high computing power and memory resources which are not available in embedded devices for on-line monitoring, also, to the use of signal processing techniques that generate volumes of data difficult to transfer to control centres, where they could be processed. This paper proposes the use of a recent methodology known as the harmonic order tracking analysis (HOTA) that solve these problems to for the diagnosis of induction generators. This approach can be implemented in low cost digital devices; the resultant patterns are very simple and easily interpretable, even by nonqualified personnel. Moreover, these patterns are characterized by a very low number of parameters, which make easy their transmission to remote control centres. In this paper the practical application of this approach is proposed using a laboratory test bed.This work was supported by the Spanish "Ministerio de Economía y Competitividad" in the framework of the "Programa Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad¿ (project reference DPI2014-60881-R)Pérez-Cruz, J.; Pérez Vázquez, M.; Pineda-Sanchez, M.; Puche-Panadero, R.; Sapena-Bano, A. (2017). The Harmonic Order Tracking Analysis (HOTA) for the Diagnosis of Induction Generators Working Under Steady State Regime. DEStech Publications. 1864-1869. http://hdl.handle.net/10251/139470S1864186

Cost-Effective Reduced Envelope of the Stator Current via Synchronous Sampling for the Diagnosis of Rotor Asymmetries in Induction Machines Working at Very Low Slip

[EN] Fault diagnosis of rotor asymmetries of induction machines (IMs) using the stator current relies on the detection of the characteristic signatures of the fault harmonics in the current spectrum. In some scenarios, such as large induction machines running at a very low slip, or unloaded machines tested offline, this technique may fail. In these scenarios, the fault harmonics are very close to the frequency of the fundamental component, and have a low amplitude, so that they may remain undetected, buried under the fundamental's leakage, until the damage is severe. To avoid false positives, a proven approach is to search for the fault harmonics in the current envelope, instead of the current itself, because in this case the spectrum is free from the leakage of the fundamental. Besides, the fault harmonics appear at a very low frequency. Nevertheless, building the current spectrum is costly in terms of computing complexity, as in the case of the Hilbert transform, or hardware resources, as in the need for simultaneously sampling three stator currents in the case of the extended current Park's vector approach (EPVA). In this work, a novel method is proposed to avoid this problem. It is based on sampling a phase current just twice per current cycle, with a fixed delay with respect to its zero crossings. It is shown that the spectrum of this reduced set of current samples contains the same fault harmonics as the spectrum of the full-length current envelope, despite using a minimal amount of computing resources. The proposed approach is cost-effective, because the computational requirements for building the current envelope are reduced to less than 1% of those required by other conventional methods, in terms of storage and computing time. In this way, it can be implemented with low-cost embedded devices for on-line fault diagnosis. The proposed approach is introduced theoretically and validated experimentally, using a commercial induction motor with a broken bar under different load and supply conditions. Besides, the proposed approach has been implemented on a low-cost embedded device, which can be accessed on-line for remote fault diagnosis.This research was funded by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i - Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M. (2019). 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IEEE Transactions on Energy Conversion, 24(1), 52-59. doi:10.1109/tec.2008.2003207Mirzaeva, G., & Saad, K. I. (2018). Advanced Diagnosis of Stator Turn-to-Turn Faults and Static Eccentricity in Induction Motors Based on Internal Flux Measurement. IEEE Transactions on Industry Applications, 54(4), 3961-3970. doi:10.1109/tia.2018.2821098Mirzaeva, G., & Saad, K. I. (2018). Advanced Diagnosis of Rotor Faults and Eccentricity in Induction Motors Based on Internal Flux Measurement. IEEE Transactions on Industry Applications, 54(3), 2981-2991. doi:10.1109/tia.2018.2805730Jian, X., Li, W., Guo, X., & Wang, R. (2019). Fault Diagnosis of Motor Bearings Based on a One-Dimensional Fusion Neural Network. Sensors, 19(1), 122. doi:10.3390/s19010122Yan, X., Sun, Z., Zhao, J., Shi, Z., & Zhang, C.-A. (2019). Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images. Sensors, 19(2), 244. doi:10.3390/s19020244Martinez, J., Belahcen, A., & Muetze, A. (2017). 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A New Algorithm for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Fed PWM Motor Drives by the Reference Current Errors. IEEE Transactions on Industrial Electronics, 60(8), 3496-3505. doi:10.1109/tie.2012.2188877Naha, A., Samanta, A. K., Routray, A., & Deb, A. K. (2017). Low Complexity Motor Current Signature Analysis Using Sub-Nyquist Strategy With Reduced Data Length. IEEE Transactions on Instrumentation and Measurement, 66(12), 3249-3259. doi:10.1109/tim.2017.2737879Moussa, M. A., Boucherma, M., & Khezzar, A. (2017). A Detection Method for Induction Motor Bar Fault Using Sidelobes Leakage Phenomenon of the Sliding Discrete Fourier Transform. IEEE Transactions on Power Electronics, 32(7), 5560-5572. doi:10.1109/tpel.2016.2605821Shahbazi, M., Saadate, S., Poure, P., & Zolghadri, M. (2016). Open-circuit switch fault tolerant wind energy conversion system based on six/five-leg reconfigurable converter. Electric Power Systems Research, 137, 104-112. doi:10.1016/j.epsr.2016.04.004Kamel, T., Biletskiy, Y., & Chang, L. (2015). Fault Diagnoses for Industrial Grid-Connected Converters in the Power Distribution Systems. IEEE Transactions on Industrial Electronics, 62(10), 6496-6507. doi:10.1109/tie.2015.2420627Nguyen, H., Kim, J., & Kim, J.-M. (2018). Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds. Sensors, 18(5), 1389. doi:10.3390/s1805138