Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle Swarm Optimization
|
摘要
A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10~(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.
A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10~(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.
A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar(BRB) fault diagnosis of induction motors. Discrete Fourier transform(DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence e ect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm(called TR-MBPSO) based on a modified bare-bones particle swarm optimization(BPSO) and trust region(TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10~(-4), but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.
引文
[1]S Bindu, V V Thomas. Diagnoses of internal faults of three phase squirrel cage induction motor–A review. Proceedings of the IEEE International Conference on Advances in Energy Conversion Technologies, Manipal, India,January 23–25, 2014:48–54.
[2]V Ghorbanian, J Faiz. A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes. Mechanical Systems and Signal Process, 2015, 54(1):427–456.
[3]M R Mehrjou, N Mariun, M H Marhaban, et al. Rotor fault condition monitoring techniques for squirrel-cage induction machine–A review.Mechanical Systems and Signal Process, 2011, 25(8):2827–2848.
[4]A Bellini, A Yazidi, F Filippetti, et al. High frequency resolution techniques for rotor fault detection of induction machines. IEEE Transactions on Industrial Electronics, 2008, 55(12):4200–4209.
[5]A F Aimer, A H Boudinar, N Benouzza, et al. Simulatioin and experimental study of induction motor broken rotor bars fault diagnosis using stator current spectrogram. Proceedings of the IEEE International Conference on Control, Engineering and Information Technology, Tlemcen, Algeria, May25–27, 2015:1–7.
[6]M Pineda-Sanchez, M Riera-Guasp, J Perez-Cruz, et al. Transient motor current signature analysis via modulus of the continuous complex wavelet:a pattern approach. Energy Conversion&Management, 2013, 73(5):26–36.
[7]P Karvelis, G Georgoulas, I P Tsoumas, et al. A symbolic representation approach for the diagnosis of broken rotor bars in induction motors. IEEE Transactions on Industrial Informatics, 2015, 11(5):1028–1037.
[8]H Keskesa, A Brahama, Z Lachiri. Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM. Electric Power Systems Research, 2013, 97(2):151–157.
[9]C Li, M Liang. A generalized synchrosqueezing transform for enhancing signal time-frequency representation. Signal Processing, 2012, 92(9):2264–2274.
[10]Z Feng, X Chen, M Liang. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. Mechanical Systems and Signal Processing, 2015,52(1):360–375.
[11]C Li, V Sanchez, G Zutita, et al. Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by timefrequency ridge enhancement. ISA Transactions, 2016, 60(1):274–284.
[12]P Tao, C Li, Y X Sheng, et al. A synchrosqueezing transform based method for rotor fault diagnosis of squirrel-cage induction motors. Proceedings of the IEEE International Conference on Control, Xi’an, China, July 26, 2013:6261–6266.
[13]P Puche-Panadero, M Pineda-Sanchez, M Riera-Guasp, et al. Improved resolution of the MCSA method via Hilbert transform, enabling the diagnosis of rotor asymmetries at very low slip. IEEE Transactions on Energy Conversion, 2009, 24(1):52–59.
[14]D F Piresa, V F Pires, J F Martins, et al. Rotor cage fault diagnosis in threephase induction motors based on a current and virtual flux approach.Energy Conversion&Management, 2009, 50(4):1026–1032.
[15]B Q Xu, L LSun. Concurrent discrimination of rotor fault and load oscillation in induction motors. Proceedings of the CSEE, 2016, 36(23):6518–6527.(in Chinese)
[16]F Gu, T Wang, A Alwodai, et al. A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals. Mechanical Systems and Signal Processing, 2014, 50(51):400–413.
[17]Y H Kim, Y W Youn, D H Hwang, et al. High-resolution parameter estimation method to identify broken rotor bar faults in induction motors. IEEE Transactions on Industrial Electronics, 2013, 60(9):4103–4117.
[18]B Q Xu, L LSun, H M Li. A detection method for rotor fault induction motors based on high frequency resolution spectrum estimation technique and optimization algorithm. Proceedings of the CSEE, 2013, 33(3):140–147.(in Chinese)
[19]L P Shi, P P Wang, Y J Hu, et al. Broken rotor bar fault diagnosis of induction motors based on bare-bone particle swarm optimization and support vector machine. Transactions of China Electrotechnical Society, 2014,29(1):147–153.(in Chinese)
[20]X Wen, L Shao, Y Xue, et al. A rapid learning algorithm for vehicle classification. Information Sciences, 2015, 29(5):395–406.
[21]B Gu, V S Sheng, Z Wang, et al. Incremental learning forν-support vector regression. Neural Networks, 2015, 67(7):140–150.
[22]B Gu, V S Sheng, K Y Tay, et al. Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks&Learning Systems, 2015, 26(7):1403–1416.
[23]B Gu, X Sun, V S Sheng. Structural minimaxprobability machine.IEEE Transactions on Neural Networks&Learning Systems, 2016, 28(7):1646–1656.
[24]Y Lin, H Zhao, H Ding. Solution of inverse kinematics for general robot manipulators based on multiple population genetic algorithm. Journal of Mechanical Engineering, 2017, 53(3):1–8.(in Chinese)
[25]H W Tang, W Sun, W Y Zhang, et al. Wavelet neural network method based on particle swarm optimization for obstacle recognition of power line deicing robot. Journal of Mechanical Engineering, 2017, 53(13):55–63.(in Chinese)
[26]J Kennedy, R C Eberhart. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Piscataway, New Jersey,USA, November 27–December 1, 1995:1942–1948.
[27]J Kennedy. Bare bones particle swarms. Proceedings of the IEEE International Conference on Swarm Intelligence Symposium, Indiana, USA, April 26,2003:80–87.
[28]M J D Powell. On the global convergence of trust region algorithms for unconstrained optimization. Mathematical Programming, 1984, 29(3):297–303.
[29]F J Solis, R J Wets. Minimization by random search techniques. Mathematics of Operations Research, 1981, 6(1):19–30.
[2]V Ghorbanian, J Faiz. A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes. Mechanical Systems and Signal Process, 2015, 54(1):427–456.
[3]M R Mehrjou, N Mariun, M H Marhaban, et al. Rotor fault condition monitoring techniques for squirrel-cage induction machine–A review.Mechanical Systems and Signal Process, 2011, 25(8):2827–2848.
[4]A Bellini, A Yazidi, F Filippetti, et al. High frequency resolution techniques for rotor fault detection of induction machines. IEEE Transactions on Industrial Electronics, 2008, 55(12):4200–4209.
[5]A F Aimer, A H Boudinar, N Benouzza, et al. Simulatioin and experimental study of induction motor broken rotor bars fault diagnosis using stator current spectrogram. Proceedings of the IEEE International Conference on Control, Engineering and Information Technology, Tlemcen, Algeria, May25–27, 2015:1–7.
[6]M Pineda-Sanchez, M Riera-Guasp, J Perez-Cruz, et al. Transient motor current signature analysis via modulus of the continuous complex wavelet:a pattern approach. Energy Conversion&Management, 2013, 73(5):26–36.
[7]P Karvelis, G Georgoulas, I P Tsoumas, et al. A symbolic representation approach for the diagnosis of broken rotor bars in induction motors. IEEE Transactions on Industrial Informatics, 2015, 11(5):1028–1037.
[8]H Keskesa, A Brahama, Z Lachiri. Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM. Electric Power Systems Research, 2013, 97(2):151–157.
[9]C Li, M Liang. A generalized synchrosqueezing transform for enhancing signal time-frequency representation. Signal Processing, 2012, 92(9):2264–2274.
[10]Z Feng, X Chen, M Liang. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. Mechanical Systems and Signal Processing, 2015,52(1):360–375.
[11]C Li, V Sanchez, G Zutita, et al. Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by timefrequency ridge enhancement. ISA Transactions, 2016, 60(1):274–284.
[12]P Tao, C Li, Y X Sheng, et al. A synchrosqueezing transform based method for rotor fault diagnosis of squirrel-cage induction motors. Proceedings of the IEEE International Conference on Control, Xi’an, China, July 26, 2013:6261–6266.
[13]P Puche-Panadero, M Pineda-Sanchez, M Riera-Guasp, et al. Improved resolution of the MCSA method via Hilbert transform, enabling the diagnosis of rotor asymmetries at very low slip. IEEE Transactions on Energy Conversion, 2009, 24(1):52–59.
[14]D F Piresa, V F Pires, J F Martins, et al. Rotor cage fault diagnosis in threephase induction motors based on a current and virtual flux approach.Energy Conversion&Management, 2009, 50(4):1026–1032.
[15]B Q Xu, L LSun. Concurrent discrimination of rotor fault and load oscillation in induction motors. Proceedings of the CSEE, 2016, 36(23):6518–6527.(in Chinese)
[16]F Gu, T Wang, A Alwodai, et al. A new method of accurate broken rotor bar diagnosis based on modulation signal bispectrum analysis of motor current signals. Mechanical Systems and Signal Processing, 2014, 50(51):400–413.
[17]Y H Kim, Y W Youn, D H Hwang, et al. High-resolution parameter estimation method to identify broken rotor bar faults in induction motors. IEEE Transactions on Industrial Electronics, 2013, 60(9):4103–4117.
[18]B Q Xu, L LSun, H M Li. A detection method for rotor fault induction motors based on high frequency resolution spectrum estimation technique and optimization algorithm. Proceedings of the CSEE, 2013, 33(3):140–147.(in Chinese)
[19]L P Shi, P P Wang, Y J Hu, et al. Broken rotor bar fault diagnosis of induction motors based on bare-bone particle swarm optimization and support vector machine. Transactions of China Electrotechnical Society, 2014,29(1):147–153.(in Chinese)
[20]X Wen, L Shao, Y Xue, et al. A rapid learning algorithm for vehicle classification. Information Sciences, 2015, 29(5):395–406.
[21]B Gu, V S Sheng, Z Wang, et al. Incremental learning forν-support vector regression. Neural Networks, 2015, 67(7):140–150.
[22]B Gu, V S Sheng, K Y Tay, et al. Incremental support vector learning for ordinal regression. IEEE Transactions on Neural Networks&Learning Systems, 2015, 26(7):1403–1416.
[23]B Gu, X Sun, V S Sheng. Structural minimaxprobability machine.IEEE Transactions on Neural Networks&Learning Systems, 2016, 28(7):1646–1656.
[24]Y Lin, H Zhao, H Ding. Solution of inverse kinematics for general robot manipulators based on multiple population genetic algorithm. Journal of Mechanical Engineering, 2017, 53(3):1–8.(in Chinese)
[25]H W Tang, W Sun, W Y Zhang, et al. Wavelet neural network method based on particle swarm optimization for obstacle recognition of power line deicing robot. Journal of Mechanical Engineering, 2017, 53(13):55–63.(in Chinese)
[26]J Kennedy, R C Eberhart. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Piscataway, New Jersey,USA, November 27–December 1, 1995:1942–1948.
[27]J Kennedy. Bare bones particle swarms. Proceedings of the IEEE International Conference on Swarm Intelligence Symposium, Indiana, USA, April 26,2003:80–87.
[28]M J D Powell. On the global convergence of trust region algorithms for unconstrained optimization. Mathematical Programming, 1984, 29(3):297–303.
[29]F J Solis, R J Wets. Minimization by random search techniques. Mathematics of Operations Research, 1981, 6(1):19–30.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |