Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model

There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliabil...

Full description

Bibliographic Details
Main Authors: Wei Dai, Yongjiao Chi, Zhiyuan Lu, Meiqing Wang, Yu Zhao
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Applied Sciences
Subjects:
Online Access:http://www.mdpi.com/2076-3417/8/9/1619
id doaj-4d5925daa4f74aa0aca06e6108f099cd
record_format Article
spelling doaj-4d5925daa4f74aa0aca06e6108f099cd2020-11-24T21:21:10ZengMDPI AGApplied Sciences2076-34172018-09-0189161910.3390/app8091619app8091619Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature ModelWei Dai0Yongjiao Chi1Zhiyuan Lu2Meiqing Wang3Yu Zhao4School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaThere is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.http://www.mdpi.com/2076-3417/8/9/1619performance degradationdegradation pathreliability assessment
collection DOAJ
language English
format Article
sources DOAJ
author Wei Dai
Yongjiao Chi
Zhiyuan Lu
Meiqing Wang
Yu Zhao
spellingShingle Wei Dai
Yongjiao Chi
Zhiyuan Lu
Meiqing Wang
Yu Zhao
Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model
Applied Sciences
performance degradation
degradation path
reliability assessment
author_facet Wei Dai
Yongjiao Chi
Zhiyuan Lu
Meiqing Wang
Yu Zhao
author_sort Wei Dai
title Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model
title_short Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model
title_full Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model
title_fullStr Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model
title_full_unstemmed Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model
title_sort research on reliability assessment of mechanical equipment based on the performance–feature model
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2018-09-01
description There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.
topic performance degradation
degradation path
reliability assessment
url http://www.mdpi.com/2076-3417/8/9/1619
work_keys_str_mv AT weidai researchonreliabilityassessmentofmechanicalequipmentbasedontheperformancefeaturemodel
AT yongjiaochi researchonreliabilityassessmentofmechanicalequipmentbasedontheperformancefeaturemodel
AT zhiyuanlu researchonreliabilityassessmentofmechanicalequipmentbasedontheperformancefeaturemodel
AT meiqingwang researchonreliabilityassessmentofmechanicalequipmentbasedontheperformancefeaturemodel
AT yuzhao researchonreliabilityassessmentofmechanicalequipmentbasedontheperformancefeaturemodel
_version_ 1726000597293858816