A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery

Rolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied. A precise and reliable remaining useful life prediction result provides valuable information for dec...

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Main Authors: Hailong Lin, Zihao Lei, Guangrui Wen, Xiaojun Tian, Xin Huang, Jinsong Liu, Haoxuan Zhou, Xuefeng Chen
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2021/6806319
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author Hailong Lin
Zihao Lei
Guangrui Wen
Xiaojun Tian
Xin Huang
Jinsong Liu
Haoxuan Zhou
Xuefeng Chen
author_facet Hailong Lin
Zihao Lei
Guangrui Wen
Xiaojun Tian
Xin Huang
Jinsong Liu
Haoxuan Zhou
Xuefeng Chen
author_sort Hailong Lin
collection DOAJ
description Rolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied. A precise and reliable remaining useful life prediction result provides valuable information for decision-makers, which is essential to ensure the safety and reliability of mechanical systems. Generally, the RUL label is considered to be an ideal life curve, which is the benchmark for RUL prediction. However, the existing label construction methods make more use of expert experience and seldom mine knowledge from data and combine experience to assist in constructing a health index (HI). In this paper, a novel and simple approach of label construction is proposed for predicting the RUL accurately. More specifically, the degradation index of the multiscale frequency domain is first extracted. Furthermore, the fuzzy C-means (FCM) algorithm is innovatively used to divide the degradation data into several stages to obtain the turning point of degradation. Then, a nonlinear degradation index, the RUL label with the turning point, was constructed based on principal component analysis (PCA). Finally, the recurrent neural network (RNN) is used for prediction and verification. In order to verify the effectiveness of the proposed approach, two different bearing lifecycle datasets are gathered and analyzed. The analysis result confirms that the proposed method is able to achieve a better performance, which outperforms some existing methods.
format Article
id doaj-art-3b61d712a947405a93b4c7beb75a2879
institution Kabale University
issn 1875-9203
language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Shock and Vibration
spelling doaj-art-3b61d712a947405a93b4c7beb75a28792025-02-03T01:25:21ZengWileyShock and Vibration1875-92032021-01-01202110.1155/2021/6806319A Novel Approach of Label Construction for Predicting Remaining Useful Life of MachineryHailong Lin0Zihao Lei1Guangrui Wen2Xiaojun Tian3Xin Huang4Jinsong Liu5Haoxuan Zhou6Xuefeng Chen7SDIC Biotechnology Investment Co., Ltd.School of Mechanical EngineeringSchool of Mechanical EngineeringSDIC Biotechnology Investment Co., Ltd.School of Mechanical EngineeringSDIC Biotechnology Investment Co., Ltd.School of Mechanical EngineeringSchool of Mechanical EngineeringRolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied. A precise and reliable remaining useful life prediction result provides valuable information for decision-makers, which is essential to ensure the safety and reliability of mechanical systems. Generally, the RUL label is considered to be an ideal life curve, which is the benchmark for RUL prediction. However, the existing label construction methods make more use of expert experience and seldom mine knowledge from data and combine experience to assist in constructing a health index (HI). In this paper, a novel and simple approach of label construction is proposed for predicting the RUL accurately. More specifically, the degradation index of the multiscale frequency domain is first extracted. Furthermore, the fuzzy C-means (FCM) algorithm is innovatively used to divide the degradation data into several stages to obtain the turning point of degradation. Then, a nonlinear degradation index, the RUL label with the turning point, was constructed based on principal component analysis (PCA). Finally, the recurrent neural network (RNN) is used for prediction and verification. In order to verify the effectiveness of the proposed approach, two different bearing lifecycle datasets are gathered and analyzed. The analysis result confirms that the proposed method is able to achieve a better performance, which outperforms some existing methods.http://dx.doi.org/10.1155/2021/6806319
spellingShingle Hailong Lin
Zihao Lei
Guangrui Wen
Xiaojun Tian
Xin Huang
Jinsong Liu
Haoxuan Zhou
Xuefeng Chen
A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery
Shock and Vibration
title A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery
title_full A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery
title_fullStr A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery
title_full_unstemmed A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery
title_short A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery
title_sort novel approach of label construction for predicting remaining useful life of machinery
url http://dx.doi.org/10.1155/2021/6806319
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