Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process

Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process

An efficient condition-based predictive spare ordering approach is the key to guarantee safe operation, improve service quality, and reduce maintenance costs under a predefined lower availability threshold. In this paper, we propose a condition-based predictive order model (CBPO) for a mechanical co...

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Main Authors: Cheng Wang, Jianxin Xu, Hongjun Wang, Zhenming Zhang
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2018/9734189
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spelling doaj-282b9d506b8d488c9e27ed6954395b9f2020-11-25T00:26:46ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/97341899734189Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation ProcessCheng Wang0Jianxin Xu1Hongjun Wang2Zhenming Zhang3School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing 100192, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, ChinaAn efficient condition-based predictive spare ordering approach is the key to guarantee safe operation, improve service quality, and reduce maintenance costs under a predefined lower availability threshold. In this paper, we propose a condition-based predictive order model (CBPO) for a mechanical component, whose degradation path is modeled as inverse Gaussian (IG) process with covariate effect. The CBPO is dependent on the remaining useful life (RUL), random lead-time, speed-up lead-time degree, and availability threshold. RUL estimation is obtained through the IG degradation process at each inspection time. Both regular lead-time and expedited lead-time considered in RUL-based spare ordering policy can be cost-effective and reduce losses caused by unexpected failure. Speed-up lead-time can meet the urgent needs for spare parts on site. The decision variable of CBPO is the spare ordering time. Based on the CBPO under the lower availability threshold constraint, the objective of this study is to determine the optimal spare ordering time such that the expected cost rate is minimized. Finally, a case study of the mechanical spindle is presented to illustrate the proposed model and sensitivity analysis on critical parameters is performed.http://dx.doi.org/10.1155/2018/9734189
collection DOAJ
language English
format Article
sources DOAJ
author Cheng Wang
Jianxin Xu
Hongjun Wang
Zhenming Zhang
spellingShingle Cheng Wang
Jianxin Xu
Hongjun Wang
Zhenming Zhang
Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process
Mathematical Problems in Engineering
author_facet Cheng Wang
Jianxin Xu
Hongjun Wang
Zhenming Zhang
author_sort Cheng Wang
title Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process
title_short Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process
title_full Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process
title_fullStr Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process
title_full_unstemmed Condition-Based Predictive Order Model for a Mechanical Component following Inverse Gaussian Degradation Process
title_sort condition-based predictive order model for a mechanical component following inverse gaussian degradation process
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2018-01-01
description An efficient condition-based predictive spare ordering approach is the key to guarantee safe operation, improve service quality, and reduce maintenance costs under a predefined lower availability threshold. In this paper, we propose a condition-based predictive order model (CBPO) for a mechanical component, whose degradation path is modeled as inverse Gaussian (IG) process with covariate effect. The CBPO is dependent on the remaining useful life (RUL), random lead-time, speed-up lead-time degree, and availability threshold. RUL estimation is obtained through the IG degradation process at each inspection time. Both regular lead-time and expedited lead-time considered in RUL-based spare ordering policy can be cost-effective and reduce losses caused by unexpected failure. Speed-up lead-time can meet the urgent needs for spare parts on site. The decision variable of CBPO is the spare ordering time. Based on the CBPO under the lower availability threshold constraint, the objective of this study is to determine the optimal spare ordering time such that the expected cost rate is minimized. Finally, a case study of the mechanical spindle is presented to illustrate the proposed model and sensitivity analysis on critical parameters is performed.
url http://dx.doi.org/10.1155/2018/9734189
work_keys_str_mv AT chengwang conditionbasedpredictiveordermodelforamechanicalcomponentfollowinginversegaussiandegradationprocess
AT jianxinxu conditionbasedpredictiveordermodelforamechanicalcomponentfollowinginversegaussiandegradationprocess
AT hongjunwang conditionbasedpredictiveordermodelforamechanicalcomponentfollowinginversegaussiandegradationprocess
AT zhenmingzhang conditionbasedpredictiveordermodelforamechanicalcomponentfollowinginversegaussiandegradationprocess
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