IEEE Transactions on Industrial Informatics· 2016Q1
A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem
- 341citations
- Q1SCImago
- 2016year
Short summary
A novel two-stage data-driven method estimates bearing degradation by comparing vibration signal statistics to a healthy state, then uses an enhanced Kalman filter and expectation-maximization algorithm to predict remaining useful life (RUL).
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Key points
- Proposes a two-stage data-driven strategy for bearing prognostics.
- Degradation is estimated by comparing vibration signal statistics to a healthy state.
- Remaining Useful Life (RUL) is adaptively estimated using an enhanced Kalman filter and expectation-maximization algorithm.
- The method was validated on a real-world bearing degradation problem.
AI-generated from the title and abstract; the full text is not read.
Abstract
Prognostics of the remaining useful life (RUL) has emerged as a critical technique for ensuring the safety, availability, and efficiency of a complex system. To gain a better prognostic result, degradation information is quite useful because it can reflect the health status of a system. However, due to the lack of accurate information about the plants' degradation, the prognostic model is usually not well established. To solve this problem, this paper proposes a two-stage strategy that is in the context of data-driven modeling to predict the future health status of a bearing, where the degradation information was estimated by calculating the deviation of multiple statistics of vibration signals of a bearing from a known healthy state. Then, a prediction stage based on an enhanced Kalman filter and an expectation-maximization algorithm were used to estimate the RUL of the bearing adaptively. To verify the effectiveness of the proposed approach, a real-bearing degradation problem was implemented.
The authors' abstract, as published at the source. IEEE Transactions on Industrial Informatics, 2016 · DOI ↗
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Field: Safety, Risk, Reliability and Quality
Safety, Risk, Reliability and QualityEngineering