Diagnosis Model of Motor Fault of Precooled Air Conditioning Unit Based on Multivariable LSTM

Abstract

The motor failure of precooled air conditioning unit (PAU) affects the operation of HVAC system directly. Traditional fault detection methods based on frequency analysis of vibration signals need high sampling frequency. However, in some actual operation and maintenance, the sampling frequency of related data is lower, it is difficult to meet the needs. In this paper, a PAU motor fault diagnosis model is constructed based on long short-term memory neural network (LSTM) combined with in-depth learning technology. The temperature of motor shell is an important symbol of motor fault. Therefore, effective features are extracted by analyzing the data characteristics. LSTM method is used to predict the motor shell temperature, and the motor fault detection and diagnosis are carried out according to the predicted residual threshold. According to the computation, the diagnostic accuracy of fault data is 100%, and the false alarm rate of fault-free data is 0.3%. The results show that the model has stronger generalization ability, higher prediction accuracy.