Health Assessment of Rolling Bearings Based on Multivariate State Estimation and Reliability Analysis
Source abstract
Abstract
Rolling bearing is an indispensable part of mechanical rotating parts, which plays an important role in reducing friction and ensuring the rotation accuracy of rotating parts. It is necessary to carry out a health assessment of the bearing. Current health assessment methods for rolling bearings only extract strongly related feature indicators and input them into the health assessment model without considering the profound impact external conditions have on the fluctuation of feature indicators, which will lead to an inaccurate health assessment. Besides, most methods evaluating the health of rolling bearings only consider the real-time index data but do not make full use of bearing maintenance data for reliability modeling and analysis, actually reducing the hierarchy and rationality of the health assessment. Therefore, this paper combines multivariate state estimation (MSET) and reliability analysis to evaluate the health of rolling bearings. Firstly, the health baseline of the rolling bearing under multi-speed conditions is established based on MSET, which collects the history health data of rolling bearings under various working conditions and learns the impact of working conditions on health data. Subsequently, Mahalanobis distance is used to measure the degree of deviation from the health baseline, and calculated Mahalanobis distance is input into the health mapping function to get the initial health score. Finally, combined with the reliability analysis correcting the initial score, the final health score is obtained, which can provide data support for intelligent operation and maintenance and a decision-making basis for equipment maintenance. The proposed health assessment method is validated using the bearing dataset from Case Western Reserve University and historical failure data of rolling bearings. The proposed method reduces speed-related influences in bearing health evaluation, dynamically adjusting the health assessment result through the reliability model to track performance degradation throughout the bearing’s service life.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Engineering Diagnostics and Reliability · Evaluation and Optimization Models
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, China
Funding
National Natural Science Foundation of China
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