Reciprocating compressor health monitoring based on BSInformer with deep convolutional AutoEncoder

Abstract

A composite model combining deep convolutional autoencoder and balanced sparse sampling Informer (MCA-BSInformer) is proposed to solve the problem that reciprocating compressors are difficult to achieve long-term accurate health monitoring. By introducing the Maximum Mean Discrepancy (MMD) difference term into the deep convolutional autoencoder, the distribution of noise data in the multi-source signal of the compressor become more similar to the distribution of normal training data. Thus, effective low-dimensional features containing spatial information in the data can be obtained to reduce the impact of noise data. In addition, a balanced sparse sampling algorithm is integrated into the Informer model to achieve adaptive sparse sampling. The improved Informer model effectively solves the problem that the model reduces the prediction performance while pursuing efficiency. The experimental results demonstrate that the prediction accuracy of the composite model for the health status of reciprocating compressors is higher than that of other mainstream models.