Multi-scale fusion convolutional neural network model for leakage size inversion of compressor valves

Abstract Valve leakage can cause reciprocating air compressors to lose operating performance or even shut down, and its leakage size cannot be directly measured. Therefore, in order to be able to achieve an accurate assessment of the valve leakage size, this paper proposes a multi-scale fusion convolutional neural network (MSFCNN)-based air compressor valve leakage size inversion model for quantifying the size of the valve leakage. The model is designed with micro-feature and macro-feature extraction channels. The macro-feature channel takes the acoustic emission data features with strong correlation coefficients with the valve leakage size as inputs, while the micro-feature channel uses wavelet decomposition to obtain the different frequency features of the acoustic emission signals, which can extract more detailed feature information. Then, through the network structure of MSFCNN, the features of these two channels are fused to achieve the comprehensive extraction and analysis of multi-scale features of acoustic emission signals, and finally the inversion of valve leakage size. In the model training phase, an automatic windowing technique is used to ensure the integrity and accuracy of the input signals. The experimental results show that the proposed model exhibits excellent performance in acoustic emission-based leakage size inversion compared to other models, which provides an important basis for valve leakage condition assessment and predictive maintenance of reciprocating air compressors to ensure their normal and efficient operation.