Fault Detection of Permanent Magnet Synchronous Motor Based on Deep Learning Method

This paper proposes a deep learning algorithm for motor fault detection. Based on the Long Short-Term Memory (LSTM), one of the deep learning algorithm, catching the three-phase current values and the information of electrical angle in the previous sampling instants, the three-phase current value at the next sampling instant can be predicted in real time; the predicted error is not affected by torque fluctuations. Therefore, the operating status of the motor can be observed in real time. The simulation results show that the error waveform amplitude is very small when the motor is running normally with the load torque randomly fluctuating, and the error amplitude will increase sharply when the motor is going to be broken down.

[1]  Imed Jlassi,et al.  Current sensor fault detection and isolation method for PMSM drives, using average normalised currents , 2016 .

[2]  A. Adouni,et al.  A DC motor fault detection, isolation and identification based on a new architecture Artificial Neural Network , 2016, 2016 5th International Conference on Systems and Control (ICSC).

[3]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[4]  Li Liu,et al.  Neural network based fault detection of PMSM stator winding short under load fluctuation , 2008, 2008 13th International Power Electronics and Motion Control Conference.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Mark Sumner,et al.  Fault detection for PMSM motor drive systems by monitoring inverter input currents , 2017 .

[7]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[8]  Kyeong-Hwa Kim,et al.  Simple Online Fault Detecting Scheme for Short-Circuited Turn in a PMSM Through Current Harmonic Monitoring , 2011, IEEE Transactions on Industrial Electronics.

[9]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[10]  Meng Li,et al.  Review of fault diagnosis of PMSM drive system in electric vehicles , 2017, 2017 36th Chinese Control Conference (CCC).

[11]  Shichuan Ding,et al.  High-resistance connection fault severity detection in a permanent magnet synchronous machine drive system , 2017, 2017 20th International Conference on Electrical Machines and Systems (ICEMS).

[12]  Shuai Li,et al.  Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Zhao Yu,et al.  Time-frequency analysis based on BLDC motor fault detection using Hermite S-method , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE).

[14]  Abhisek Ukil,et al.  Model-based failure prediction for electric machines using particle filter , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

[15]  Jun Jo,et al.  Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection , 2017, 2017 IEEE International Conference on Big Data (Big Data).