Multisensor Information Fusion for Fault Diagnosis of Axial Piston Pump Based on Improved WPD and SSA-KSTTM
Dong‐Ning Chen, Zi-Yu Zhou, Dong-Bo Hu, Wen-Ping Liu, Jitao Liu, Ya-Nan Chen
Abstract
Axial piston pumps have been extensively applied in hydraulic systems, and their reliability takes on critical significance in the stable operation of the entire hydraulic system. How to extract fault feature parameters and identify faults in axial piston pumps turns out to be vital in ensuring the safety and reliability of the hydraulic system. In this study, a fault diagnosis method of multi-sensor information fusion for axial piston pumps is proposed based on improved wavelet packet decomposition (WPD) and kernelized support tensor training machine (KSTTM). Four-layer WPD is utilized to obtain different frequency band features of vibration, sound, and pressure signals. The intra-class and inter-class distances are then introduced for quantitative evaluation of the decomposition results, which enables the selection of the optimal wavelet basis function. Subsequently, a new structured feature representation model, structure tensor space model, is proposed to fuse the features of multiple signals. A multi-class classifier model of KSTTM optimized by multi-objective salp swarm algorithm (SSA) is developed to identify the fault states of the pumps. The experimental results show that multi-sensor information fusion can obtain better results compared with single sensor. As indicated by the results of the in-depth experiments, the tensor-based method is capable of effectively increasing the classification accuracy, and it is robust to small sample size problems compared with the conventional vector-based method.