Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model

Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model

A trend prediction method based on the Pchip-EEMD-GM(1,1) to predict the remaining useful life (RUL) of rolling bearings was proposed in this paper. Firstly, the dimension of the extracted features was reduced by the KPCA dimensionality reduction method, and the WPHM model parameters were estimated...

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Main Authors: Fengtao Wang, Xiaofei Liu, Chenxi Liu, Hongkun Li, Qingkai Han
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2018/3013684
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spelling doaj-00fd926d3c594614aee5d70639212ffb2020-11-24T21:37:09ZengHindawi LimitedShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/30136843013684Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) ModelFengtao Wang0Xiaofei Liu1Chenxi Liu2Hongkun Li3Qingkai Han4School of Mechanical Engineering, Dalian University of Technology, Dalian, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian, ChinaSchool of Mechanical Engineering, Dalian University of Technology, Dalian, ChinaA trend prediction method based on the Pchip-EEMD-GM(1,1) to predict the remaining useful life (RUL) of rolling bearings was proposed in this paper. Firstly, the dimension of the extracted features was reduced by the KPCA dimensionality reduction method, and the WPHM model parameters were estimated via the kernel principal components. Secondly, the hazard rate was calculated at each time, and the Pchip interpolation method was used to obtain the uniformly spaced interpolation data series. Then the main trend of signal was obtained through the EEMD method to fit the GM(1,1) prediction model. Finally, the GM (1,1) method was used to predict the remaining life of the rolling bearing. The full life test of rolling bearing was provided to demonstrate that the method predicting the hazard data directly has the higher accuracy compared with predicting the covariates, and the results verified the feasibility and effectiveness of the proposed method for predicting the remaining life.http://dx.doi.org/10.1155/2018/3013684
collection DOAJ
language English
format Article
sources DOAJ
author Fengtao Wang
Xiaofei Liu
Chenxi Liu
Hongkun Li
Qingkai Han
spellingShingle Fengtao Wang
Xiaofei Liu
Chenxi Liu
Hongkun Li
Qingkai Han
Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model
Shock and Vibration
author_facet Fengtao Wang
Xiaofei Liu
Chenxi Liu
Hongkun Li
Qingkai Han
author_sort Fengtao Wang
title Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model
title_short Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model
title_full Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model
title_fullStr Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model
title_full_unstemmed Remaining Useful Life Prediction Method of Rolling Bearings Based on Pchip-EEMD-GM(1, 1) Model
title_sort remaining useful life prediction method of rolling bearings based on pchip-eemd-gm(1, 1) model
publisher Hindawi Limited
series Shock and Vibration
issn 1070-9622
1875-9203
publishDate 2018-01-01
description A trend prediction method based on the Pchip-EEMD-GM(1,1) to predict the remaining useful life (RUL) of rolling bearings was proposed in this paper. Firstly, the dimension of the extracted features was reduced by the KPCA dimensionality reduction method, and the WPHM model parameters were estimated via the kernel principal components. Secondly, the hazard rate was calculated at each time, and the Pchip interpolation method was used to obtain the uniformly spaced interpolation data series. Then the main trend of signal was obtained through the EEMD method to fit the GM(1,1) prediction model. Finally, the GM (1,1) method was used to predict the remaining life of the rolling bearing. The full life test of rolling bearing was provided to demonstrate that the method predicting the hazard data directly has the higher accuracy compared with predicting the covariates, and the results verified the feasibility and effectiveness of the proposed method for predicting the remaining life.
url http://dx.doi.org/10.1155/2018/3013684
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AT xiaofeiliu remainingusefullifepredictionmethodofrollingbearingsbasedonpchipeemdgm11model
AT chenxiliu remainingusefullifepredictionmethodofrollingbearingsbasedonpchipeemdgm11model
AT hongkunli remainingusefullifepredictionmethodofrollingbearingsbasedonpchipeemdgm11model
AT qingkaihan remainingusefullifepredictionmethodofrollingbearingsbasedonpchipeemdgm11model
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