Performance Degradation Assessment for Electrical Machines Based on SOM and Hybrid DHMM

TL;DR

This paper introduces a novel method combining SOM and hybrid DHMM to accurately assess electrical machine degradation, identify degradation states, and analyze failure mode evolution for proactive maintenance.

Contribution

It proposes a new approach integrating SOM and hybrid DHMM to address deficiencies in degradation state relationship analysis and failure mode evolution assessment.

Findings

Accurately identifies degradation states of electrical machines.

Achieves superior assessment accuracy in real-world application.

Effectively models transition processes among degradation states.

Abstract

Aimed at the timely detection of the degradation of electrical machines and the organization of active maintenance, numerous studies on performance degradation assessment have been conducted. However, previous research still suffers from two deficiencies: 1) determining the relevant relationship among diverse machine degradation states and assessing the specific degree of deterioration and 2) determining the evolutionary relationships among degradation and failure modes and assessing the failure modes corresponding to different degradation scenarios. To address these two deficiencies, a novel performance degradation assessment method is proposed. First, the self-organizing feature map (SOM) network is used to mine the latent degradation states of electrical machines. Second, the latent states are quantified according to established statistical health indexes, and by analyzing the distribution of extracted health indexes corresponding to different degradation states, the relevant transition relationships of the valid degradation states and the final evolving fault types are determined. Third, a hybrid discrete HMM is developed to fully describe the transition process among different states and assess the degradation scenario of a machine in an online manner. The results of a real application of an electric point machine show that the proposed method can identify valid degradation states and obtain a superior assessment accuracy.