Investigation on morphological filtering via enhanced adaptive time-varying structural element for bearing fault diagnosis

The accurate extraction of machine fault-related information is the premise for implementing condition-based maintenance. In vibration analysis, morphological filtering is an effective method to detect bearing fault signatures, wherein the design of structural element and the construction of morphological operator are crucial to its performance. In this paper, a generalized morphological diagonal slice operator (GMDSO) framework is established for constructing new morphological operators with strong immunity to multi-source noise. Then, by introducing high-performance morphological operators into the GMDSO framework, a specific morphological gradient diagonal slice operator (MGDSO), is designed for extracting transient signatures. To optimize the signature excavation of morphological operators and attenuate the influence of noise in selecting structural element shape and length, an enhanced adaptive time-varying structural element (EATVSE) is proposed for more exact matching fault signatures. Finally, to accurately diagnose the early faults of rolling bearings, an enhanced adaptive time-varying morphological filtering (EATVMF) is proposed in combination with MGDSO and EATVSE. The fault diagnosis capability of EATVMF is testified on simulated signals, experimental signals, and bearing accelerated degradation datasets, and compared with five existing methods. The results demonstrate that EATVMF has excellent transient signature excavation and noise elimination capabilities under strong interference noise, and outperforms comparison methods.