Fault Classification of Stator Current Sensors using LSTM Neural Network in an Induction Motor Drive

Popularly used induction motor (IM) drives controlled by vector methods must comply with the requirements for operational safety. This requires fault-tolerant control (FTC) algorithms. FTC systems work with diagnostic algorithms to detect and analyse the fault level. One of the key components in an AC motor drive vector control system are the stator current sensors, which, like other components, can fail. Developing diagnostic methods to detect, locate, and assess the type of damage to the current sensors is therefore desirable. In this paper, we present a stator current sensor fault classification algorithm using a Long Short-Term Memory (LSTM) recurrent neural network. The developed method is based on an existing solution that used a multilayer perceptron (MLP) neural network for classification purposes. The use of the LSTM network allowed the number of hyperparameters to be reduced from 441 (MLP) to 251 (LSTM), while maintaining high classification accuracy (LSTM - 96.8%, MLP - 96.8%). Network testing was carried out on data from simulations carried out in the MATLAB/SIMULINK environment in a vector-controlled IM drive system. The test results obtained are important in terms of the optimisation of the size of the neural network, which is directly related to the future hardware implementation.