Research on Intelligent Fault Diagnosis of Reciprocating Compressor Valves Based on Multi-Source Information Fusion with Improved SWD

Aiming at solving the problems of the complex impact vibration characteristics of reciprocating compressor valves, the inability of a single signal to fully characterize state characteristics, and the difficulty of effectively extracting and fusing feature information from multi-source signals, this paper constructs a fault diagnosis and prediction model combining Improved Swarm Decomposition (ISWD) and t-SNE dimensionality reduction and fusion with a Multi-scale Convolutional Neural Network–Bidirectional Gated Recurrent Unit (MCNN-BiGRU) based on multi-source signals and applies it to the fault diagnosis and pattern recognition prediction of reciprocating compressor valves. Firstly, atom search optimization (ASO) is adopted to optimize the decomposition parameters of Swarm Decomposition (SWD) to obtain the ISWD algorithm, which is applied to decompose the multi-source signals of compressors to extract the oscillating components (OCs). Secondly, the correlation coefficient method is used to screen the OCs and conduct signal reconstruction, and various entropy feature values are extracted from the reconstructed signals to form an initial feature set. Then the t-SNE algorithm is employed to perform dimensionality reduction and fusion on the initial feature set, yielding a more concise and representative fused feature set. Finally, the fused feature set after dimensionality reduction and fusion is input into the MCNN-BiGRU model for training, so as to realize the pattern recognition and prediction of valve faults. The effectiveness and superiority of this method in the fault diagnosis of reciprocating compressor valves are verified through numerical simulation and experimental analysis.