Health Monitoring and Drift Detection of Bearing Using Direct Density Ratio Estimation
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Abstract
Prognostics and health management (PHM) has been widely employed for condition monitoring, fault diagnosis and failure prediction in mechanical systems. However, the presence of uncertainty and transient fluctuations in condition monitoring data makes the precise detection of degradation challenging. This paper presents a novel direct density ratio estimation (DDRE) method that computes the change score of the health indicator to detect degradation. The approach continuously computes the change score between two sliding windows using noise-assisted relative unconstrained least-squares importance fitting (NARuLSIF). This study does not rely solely on magnitude of the DDRE-based dissimilarity score; instead, it analyses the dynamic behaviors of the change score to categorize degradations into steady and progressive types. Additionally, this research identifies the onset of runaway failures, referred to as the initial degradation point (IDP), which is used as the starting point for remaining useful life (RUL) estimation. To validate the proposed approach, a publicly available rolling-element bearing dataset is utilized. Experimental results demonstrate the effectiveness and robustness of the proposed DDRE method for both degradation detection and selection of the IDP.
How to Cite
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Defect tracking, Change detection, Direct density ratio estimation, First prediction time
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