A hybrid deep learning model approach for performance index prediction of mechanical equipment

Abstract

To forecast the health status of mechanical equipment in industrial production, fault diagnosis systems need a fast and accurate algorithm to forecast the important performance indexes of mechanical equipment. According to the characteristics of time series, a composite variable wavelet transform, deep autoencoder and long short-term memory (CWD-LSTM) hybrid neural network forecast algorithm is proposed to carry out one-step forecast experiments on air compressor datasets. As one of the important indexes reflecting the performance of the air compressor, loading time is usually a parameter that the fault diagnosis system needs to forecast and analyze. The experimental results show that compared with the original neural network and other similar algorithms, the CWD-LSTM algorithm has obvious advantages in forecasting the loading time under a variety of detection indexes. More importantly, CWD-LSTM does not require a high update frequency of the neural network, and manufacturers do not need a frequent training model to ensure the reliability of forecast.