Machine Learning for Fault Diagnosis of Electric Motors in Actuator Systems

Electric linear or rotary actuators are the ultimate power-dense execution units in modern industrial and transportation systems, yet their dependability is directly governed by the health of the driving electric motor. To guarantee fail-safe operation of the electromechanical actuator chain, condition monitoring and fault diagnosis of the embedded motor have become indispensable. The motor fault diagnosis process can be comprehensively summarized into four key steps: signal acquisition, feature extraction, condition monitoring, and fault identification. Based on the data obtained by signal acquisition, machine learning methods can be effectively integrated into the latter three steps. Feature extraction techniques primarily revolve around autoencoders. In terms of condition monitoring technology, in-depth research has been conducted on image recognition, including the identification of two-dimensional and three-dimensional images. In terms of fault identification, various machine learning methods have been applied, such as convolutional neural networks, autoencoders, transfer learning, long short-term memory networks, and support vector machines. Finally, the potential application of the Large Language Model in motor fault diagnosis was explored.