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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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/9734189 |
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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 |
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