A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Biao Wang; Yaguo Lei; Naipeng Li; Ningbo Li

doi:10.1109/tr.2018.2882682

Abstract

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

Keywords

PrognosticsRolling-element bearingReliability (semiconductor)Bearing (navigation)VibrationEngineeringKernel (algebra)Reliability engineeringSupport vector machineConvergence (economics)Condition monitoringComputer scienceMachine learningArtificial intelligenceMathematics

Affiliated Institutions

Xi'an Jiaotong University CN

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Publication Info

Year: 2018
Type: article
Volume: 69
Issue: 1
Pages: 401-412
Citations: 1539
Access: Closed

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APA Style

                            
                                
                                    Biao Wang, 
                                
                                    Yaguo Lei, 
                                
                                    Naipeng Li
                                
                                et al.
                            
                            (2018). 
                            A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings. 
                            IEEE Transactions on Reliability
                            , 69
                            (1)
                            , 401-412.
                            https://doi.org/10.1109/tr.2018.2882682
                        

Identifiers

DOI: 10.1109/tr.2018.2882682

Data Quality

Data completeness: 81%