A comprehensive approach with DTW-driven IMF selection, multi-domain fusion, and TSA-based feature selection for compound fault diagnosis

Abstract

Fault diagnosis in industrial machinery ensures operational efficiency and prevents downtimes. However, compound faults present a significant challenge due to their complex nature. This manuscript proposes a novel methodology for identifying compound faults, integrating advanced techniques to address the challenges present in existing methods. Firstly, the ICEEMDAN algorithm is employed for signal decomposition, enabling the extraction of noise-free IMFs crucial for identifying fault signatures. A DTW-based selection criteria is introduced to enhance the accuracy of IMF selection. The Bessel Transform is utilized for time–frequency conversion. HU invariant moments are incorporated as image features, and multi-domain feature fusion is implemented to understand compound faults comprehensively. A novel TSA-based feature selection method is proposed to select optimal features from the fused feature set. The methodology’s effectiveness is evaluated through a case study of compound gear-bearing faults. The results demonstrate the superiority of the approach, with a testing accuracy of 99 %.