Two General Architectures for Intelligent Machine Performance Degradation Assessment
Markov model is of good ability to infer random events whose likelihood depends on previous events. Based on this theory, hidden Markov model serves as an extension of Markov model to present an event from observations rather than states in Markov model. Moreover, due to successful applications in s...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/676959 |
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| _version_ | 1850220484795301888 |
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| author | Yanwei Xu Aijun Xu Tancheng Xie |
| author_facet | Yanwei Xu Aijun Xu Tancheng Xie |
| author_sort | Yanwei Xu |
| collection | DOAJ |
| description | Markov model is of good ability to infer random events whose likelihood depends on previous events. Based on this theory, hidden Markov model serves as an extension of Markov model to present an event from observations rather than states in Markov model. Moreover, due to successful applications in speech recognition, it attracts much attention in machine fault diagnosis. This paper presents two architectures for machine performance degradation assessment, which can be used to minimize machine downtime, reduce economic loss, and improve productivity. The major difference between the two architectures is whether historical data are available to build hidden Markov models. In case studies, bearing data as well as available historical data are used to demonstrate the effectiveness of the first architecture. Then, whole life gearbox data without historical data are employed to demonstrate the effectiveness of the second architecture. The results obtained from two case studies show that the presented architectures have good abilities for machine performance degradation assessment. |
| format | Article |
| id | doaj-art-7aea5c628eec4c54afb7c4c2bac91c8f |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-7aea5c628eec4c54afb7c4c2bac91c8f2025-08-20T02:07:02ZengWileyShock and Vibration1070-96221875-92032015-01-01201510.1155/2015/676959676959Two General Architectures for Intelligent Machine Performance Degradation AssessmentYanwei Xu0Aijun Xu1Tancheng Xie2School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, ChinaMarkov model is of good ability to infer random events whose likelihood depends on previous events. Based on this theory, hidden Markov model serves as an extension of Markov model to present an event from observations rather than states in Markov model. Moreover, due to successful applications in speech recognition, it attracts much attention in machine fault diagnosis. This paper presents two architectures for machine performance degradation assessment, which can be used to minimize machine downtime, reduce economic loss, and improve productivity. The major difference between the two architectures is whether historical data are available to build hidden Markov models. In case studies, bearing data as well as available historical data are used to demonstrate the effectiveness of the first architecture. Then, whole life gearbox data without historical data are employed to demonstrate the effectiveness of the second architecture. The results obtained from two case studies show that the presented architectures have good abilities for machine performance degradation assessment.http://dx.doi.org/10.1155/2015/676959 |
| spellingShingle | Yanwei Xu Aijun Xu Tancheng Xie Two General Architectures for Intelligent Machine Performance Degradation Assessment Shock and Vibration |
| title | Two General Architectures for Intelligent Machine Performance Degradation Assessment |
| title_full | Two General Architectures for Intelligent Machine Performance Degradation Assessment |
| title_fullStr | Two General Architectures for Intelligent Machine Performance Degradation Assessment |
| title_full_unstemmed | Two General Architectures for Intelligent Machine Performance Degradation Assessment |
| title_short | Two General Architectures for Intelligent Machine Performance Degradation Assessment |
| title_sort | two general architectures for intelligent machine performance degradation assessment |
| url | http://dx.doi.org/10.1155/2015/676959 |
| work_keys_str_mv | AT yanweixu twogeneralarchitecturesforintelligentmachineperformancedegradationassessment AT aijunxu twogeneralarchitecturesforintelligentmachineperformancedegradationassessment AT tanchengxie twogeneralarchitecturesforintelligentmachineperformancedegradationassessment |