Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique
Abstract The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Abid Ben, F., Sallem, M., and Braham, A., 2019. Robust interpretable deep learning for intelligent fault diagnosis of induction motors, IEEE Transactions on Instrumentation and Measurement, Vol: 69, No: 6, pp: 3506–3515.
Ali Zawad, M., Shabbir, M. N. S. K., Liang, X., Zhang, Y., and Hu T., 2019. Machine learning-based fault diagnosis for single-and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Transactions on Industry Applications, Vol: 55, No: 3, pp: 2378–2391.
Amaize, P., Ignatius, K. O., Oluwasogo, E. S., Alayande, A. S., and Airoboman, A. E., 2017. Influence of power quality problem on the performance of an induction motor. American Journal of Electrical Power and Energy Systems, Vol: 4, No:4, pp: 39–44.
Bessous, N., Zouzou, S. E., Bentrah, W., Sbaa, S., and Sahraoui, M., 2018. Diagnosis of bearing defects in induction motors using discrete wavelet transform. International Journal of System Assurance Engineering and Management, Vol: 9, No: 2, pp: 335–343.
Chouhan, A., Gangsar, P., Porwal, R., Mechefske, C. K., 2021. Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions. Noise & Vibration Worldwide, Vol: 52, No: 10, pp. 323–33.
Ding, Xiaoxi, and He, Q., 2017. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, Vol: 66, No: 8, pp: 1926–1935.
Dong, Shaojiang, Xu, X., and Chen, R., 2016. Application of fuzzy C-means method and classification model of optimized K-nearest neighbor for fault diagnosis of bearing. Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol: 38, No: 8, pp: 2255–2263.
Donolo, Pablo D., Pezzani, C. M., Bossio, G. R., Angelo, C. H. D., and Donolo, M. A., 2020. Derating of induction motors due to power quality issues considering the motor efficiency class. IEEE Transactions on Industry Applications, Vol: 56, No: 2, pp: 961–969.
Ghaseminezhad, Morteza, Doroudi, A., Hosseinian, S. H., and Jalilian, A., 2017. Analysis of voltage fluctuation impact on induction motors by an innovative equivalent circuit considering the speed changes. IET Generation, Transmission & Distribution, Vol: 11, No: 2, pp: 512–519.
Ghods, Amirhossein, and Lee, H., 2016. Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors. Neurocomputing, Vol: 188, pp: 206–216.
Hassan, Ola., E., Amer, M., Ahmed, K., Abdelsalam, and Williams, B. W., 2018. Induction motor broken rotor bar fault detection techniques based on fault signature analysis–a review. IET Electric Power Applications, Vol: 12, No: 7, pp: 895–907
Hsueh, Yu-Min, Ittangihal, V. R., Wu, W., Chang, H., and Kuo, C., 2019. Fault diagnosis system for induction motors by CNN using empirical wavelet transform. Symmetry, Vol: 11, No: 10, pp: 1212.
Ibrahim, A., Anayi, F., Packianather, M., and Alomari, O. A., 2022.New hybrid invasive weed optimization and machine learning approach for fault detection. Energies, Vol: 15, No: 4, pp. 1488.
Kumar, Prashant, and Hati, A., S., 2021. Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM. ISA transactions, Vol: 111, pp: 350–359.
Kumar, R. R., Cirrincione, G., Cirrincione, M., Tortella, A., and Andriollo, M., 2021. Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks. IEEE Transactions on Energy Conversion, Vol: 36, No: 2, pp. 1070–1080.
Liu, Yiqi, and Bazzi, A. M., 2017. A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art. ISA transactions, Vol: 70, pp: 400–409.
Ma, Suliang, Chen, M., Wu, J., Wang, Y., Jia, B., and Jiang, Y., 2018. High-voltage circuit breaker fault diagnosis using a hybrid feature transformation approach based on random forest and stacked autoencoder. IEEE Transactions on Industrial Electronics, Vol: 66, No: 12, pp: 9777–9788.
Toma, N., Rafia, and Kim, J., 2020. Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Applied Sciences, Vol: 10, No: 15, pp: 5251.
Pandarakone Esakimuthu, S., Mizuno, Y., and Nakamura, H., 2016. Distinct fault analysis of induction motor bearing using frequency spectrum determination and support vector machine. IEEE Transactions on Industry Applications, Vol: 53, No: 3, pp: 3049–3056.
Su, Jinya, and Chen, W., 2017. Model-based fault diagnosis system verification using reachability analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol: 49, No: 4: 742–751.
Toma Nishat, R., Prosvirin, A. E., and Kim, J., 2020. Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, Vol: 20, No: 7, pp: 1884.
Xu, Xiaojian, Yan, X., Sheng, C., Yuan, C., Xu, D., and Yang, J., 2017. A belief rule-based expert system for fault diagnosis of marine diesel engines. IEEE transactions on systems, man, and cybernetics: systems, Vol: 50, No: 2, pp: 656–672.
Zaman, S. M. K., and Liang, X., 2021. An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning. IEEE Access, Vol: 9, pp. 7471–7482.
Zhang, Dongdong, An, R., and Wu, T., 2018. Effect of voltage unbalance and distortion on the loss characteristics of three-phase cage induction motor. IET Electric Power Applications, Vol: 12, No: 2, pp: 264–270.
Zhang, Nannan, Wu, L., Yang, J., and Guan, Y., 2018. Naive bayes bearing fault diagnosis based on enhanced independence of data. Sensors, Vol: 18, No: 2, pp: 463.
Ali Zawad, M., Shabbir, M. N. S. K., Liang, X., Zhang, Y., and Hu T., 2019. Machine learning-based fault diagnosis for single-and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Transactions on Industry Applications, Vol: 55, No: 3, pp: 2378–2391.
Amaize, P., Ignatius, K. O., Oluwasogo, E. S., Alayande, A. S., and Airoboman, A. E., 2017. Influence of power quality problem on the performance of an induction motor. American Journal of Electrical Power and Energy Systems, Vol: 4, No:4, pp: 39–44.
Bessous, N., Zouzou, S. E., Bentrah, W., Sbaa, S., and Sahraoui, M., 2018. Diagnosis of bearing defects in induction motors using discrete wavelet transform. International Journal of System Assurance Engineering and Management, Vol: 9, No: 2, pp: 335–343.
Chouhan, A., Gangsar, P., Porwal, R., Mechefske, C. K., 2021. Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions. Noise & Vibration Worldwide, Vol: 52, No: 10, pp. 323–33.
Ding, Xiaoxi, and He, Q., 2017. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, Vol: 66, No: 8, pp: 1926–1935.
Dong, Shaojiang, Xu, X., and Chen, R., 2016. Application of fuzzy C-means method and classification model of optimized K-nearest neighbor for fault diagnosis of bearing. Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol: 38, No: 8, pp: 2255–2263.
Donolo, Pablo D., Pezzani, C. M., Bossio, G. R., Angelo, C. H. D., and Donolo, M. A., 2020. Derating of induction motors due to power quality issues considering the motor efficiency class. IEEE Transactions on Industry Applications, Vol: 56, No: 2, pp: 961–969.
Ghaseminezhad, Morteza, Doroudi, A., Hosseinian, S. H., and Jalilian, A., 2017. Analysis of voltage fluctuation impact on induction motors by an innovative equivalent circuit considering the speed changes. IET Generation, Transmission & Distribution, Vol: 11, No: 2, pp: 512–519.
Ghods, Amirhossein, and Lee, H., 2016. Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors. Neurocomputing, Vol: 188, pp: 206–216.
Hassan, Ola., E., Amer, M., Ahmed, K., Abdelsalam, and Williams, B. W., 2018. Induction motor broken rotor bar fault detection techniques based on fault signature analysis–a review. IET Electric Power Applications, Vol: 12, No: 7, pp: 895–907
Hsueh, Yu-Min, Ittangihal, V. R., Wu, W., Chang, H., and Kuo, C., 2019. Fault diagnosis system for induction motors by CNN using empirical wavelet transform. Symmetry, Vol: 11, No: 10, pp: 1212.
Ibrahim, A., Anayi, F., Packianather, M., and Alomari, O. A., 2022.New hybrid invasive weed optimization and machine learning approach for fault detection. Energies, Vol: 15, No: 4, pp. 1488.
Kumar, Prashant, and Hati, A., S., 2021. Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM. ISA transactions, Vol: 111, pp: 350–359.
Kumar, R. R., Cirrincione, G., Cirrincione, M., Tortella, A., and Andriollo, M., 2021. Induction Machine Fault Detection and Classification Using Non-Parametric, Statistical-Frequency Features and Shallow Neural Networks. IEEE Transactions on Energy Conversion, Vol: 36, No: 2, pp. 1070–1080.
Liu, Yiqi, and Bazzi, A. M., 2017. A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art. ISA transactions, Vol: 70, pp: 400–409.
Ma, Suliang, Chen, M., Wu, J., Wang, Y., Jia, B., and Jiang, Y., 2018. High-voltage circuit breaker fault diagnosis using a hybrid feature transformation approach based on random forest and stacked autoencoder. IEEE Transactions on Industrial Electronics, Vol: 66, No: 12, pp: 9777–9788.
Toma, N., Rafia, and Kim, J., 2020. Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Applied Sciences, Vol: 10, No: 15, pp: 5251.
Pandarakone Esakimuthu, S., Mizuno, Y., and Nakamura, H., 2016. Distinct fault analysis of induction motor bearing using frequency spectrum determination and support vector machine. IEEE Transactions on Industry Applications, Vol: 53, No: 3, pp: 3049–3056.
Su, Jinya, and Chen, W., 2017. Model-based fault diagnosis system verification using reachability analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol: 49, No: 4: 742–751.
Toma Nishat, R., Prosvirin, A. E., and Kim, J., 2020. Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors, Vol: 20, No: 7, pp: 1884.
Xu, Xiaojian, Yan, X., Sheng, C., Yuan, C., Xu, D., and Yang, J., 2017. A belief rule-based expert system for fault diagnosis of marine diesel engines. IEEE transactions on systems, man, and cybernetics: systems, Vol: 50, No: 2, pp: 656–672.
Zaman, S. M. K., and Liang, X., 2021. An Effective Induction Motor Fault Diagnosis Approach Using Graph-Based Semi-Supervised Learning. IEEE Access, Vol: 9, pp. 7471–7482.
Zhang, Dongdong, An, R., and Wu, T., 2018. Effect of voltage unbalance and distortion on the loss characteristics of three-phase cage induction motor. IET Electric Power Applications, Vol: 12, No: 2, pp: 264–270.
Zhang, Nannan, Wu, L., Yang, J., and Guan, Y., 2018. Naive bayes bearing fault diagnosis based on enhanced independence of data. Sensors, Vol: 18, No: 2, pp: 463.
Katta, P., Kandasamy, K., Soosaimarian Peter Raj, R., Subramanian, R., & Perumal, C. (2023). Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(3), 349–365. https://doi.org/10.14201/adcaij.28435
Downloads
Download data is not yet available.
+
−