Abstract
One of the challenges in predicting the remaining useful life (RUL) of rolling element bearings (REBs) is determining a proper failure threshold (FT). In the literature, the FT is usually assumed to be a constant value of an extracted feature from the vibration signals. In this study, a degradation indicator was extracted to describe damage to REBs by applying principal component analysis (PCA) to their run-to-failure data. The relationship between this degradation indicator and the vibration peak was represented through a joint probability distribution using statistical copula models. The FT was proposed as a probability distribution based on the fluctuation increase in the vibration trend. A set of run-to-failure tests was conducted. Applying the proposed method to this dataset led to various FTs for the different failure modes that occurred. It is shown that, for inner race degradation, a higher FT can be assumed than for rolling element degradation. This could help extend the lives of REBs regarding the degrading elements. A dataset for an industrial machine was also analyzed and it is shown that the proposed model estimated a reasonable and proper FT in an actual case study.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference28 articles.
1. Predicting remaining useful life of rotating machinery based artificial neural network
2. Standard ISO 13381-1. Condition Monitoring and Diagnostics of Machines—Prognostics—Part 1: General Guidelines,2015
3. Standard ISO 10816-3. Mechanical Vibration—Evaluation of Machine Vibration by Measurements on Non-Rotating Parts—Part 3: Industrial Machines with Normal Power above 15 kW and Nominal Speeds between 120 r/min and 15000 r/min,2009
4. Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning
5. Two novel mixed effects models for prognostics of rolling element bearings
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