Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM
Online running condition monitoring of the vertical roller mill (VRM) is significant to assess the equipment performance degradation and reliability. This paper proposes a performance reliability assessment method based on autoregressive integrated moving average (ARIMA) model and hidden Markov mode...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2016.1270703 |
| _version_ | 1797716921800458240 |
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| author | Qiang Wang Yilin Fang Zude Zhou Jie Zuo Qili Xiao Shujuan Zhou |
| author_facet | Qiang Wang Yilin Fang Zude Zhou Jie Zuo Qili Xiao Shujuan Zhou |
| author_sort | Qiang Wang |
| collection | DOAJ |
| description | Online running condition monitoring of the vertical roller mill (VRM) is significant to assess the equipment performance degradation and reliability. This paper proposes a performance reliability assessment method based on autoregressive integrated moving average (ARIMA) model and hidden Markov model (HMM) using the real-time sensing monitoring signals, which is designed to analyze the running state and predict the reliability of VRM. As most faults of VRM relate to hydraulic pressure of loading system and mechanical vibration, research on hydraulic monitoring and vibration monitoring is prerequisites, which determines the sensing parameters and monitoring points, provides the data base for following reliability assessment. Then ARIMA is applied to establish the performance degradation path using the historical sensing monitoring data. Finally, the multi-observation HMM is used to estimate the reliability changing trend of the equipment, the input observations of which are the predictive data from the performance degradation model. At the end of this paper, an experiment based on the real VRM sensing monitoring data is used to verify the effectiveness of the performance reliability assessment method. The experimental result shows that the proposed method is effective for performance reliability analysis and health condition management of VRM. |
| first_indexed | 2024-03-12T08:28:39Z |
| format | Article |
| id | doaj.art-4f8f288fba5a429983c5f894acf975d5 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T08:28:39Z |
| publishDate | 2017-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-4f8f288fba5a429983c5f894acf975d52023-09-02T17:56:04ZengTaylor & Francis GroupCogent Engineering2331-19162017-01-014110.1080/23311916.2016.12707031270703Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMMQiang Wang0Yilin Fang1Zude Zhou2Jie Zuo3Qili Xiao4Shujuan Zhou5Wuhan University of TechnologyWuhan University of TechnologyWuhan University of TechnologyWuhan University of TechnologyWuhan University of TechnologyWuhan University of TechnologyOnline running condition monitoring of the vertical roller mill (VRM) is significant to assess the equipment performance degradation and reliability. This paper proposes a performance reliability assessment method based on autoregressive integrated moving average (ARIMA) model and hidden Markov model (HMM) using the real-time sensing monitoring signals, which is designed to analyze the running state and predict the reliability of VRM. As most faults of VRM relate to hydraulic pressure of loading system and mechanical vibration, research on hydraulic monitoring and vibration monitoring is prerequisites, which determines the sensing parameters and monitoring points, provides the data base for following reliability assessment. Then ARIMA is applied to establish the performance degradation path using the historical sensing monitoring data. Finally, the multi-observation HMM is used to estimate the reliability changing trend of the equipment, the input observations of which are the predictive data from the performance degradation model. At the end of this paper, an experiment based on the real VRM sensing monitoring data is used to verify the effectiveness of the performance reliability assessment method. The experimental result shows that the proposed method is effective for performance reliability analysis and health condition management of VRM.http://dx.doi.org/10.1080/23311916.2016.1270703the vertical roller millautoregressive integrated moving average modelhidden markov modelmulti-observationperformance reliability assessment |
| spellingShingle | Qiang Wang Yilin Fang Zude Zhou Jie Zuo Qili Xiao Shujuan Zhou Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM Cogent Engineering the vertical roller mill autoregressive integrated moving average model hidden markov model multi-observation performance reliability assessment |
| title | Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM |
| title_full | Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM |
| title_fullStr | Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM |
| title_full_unstemmed | Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM |
| title_short | Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM |
| title_sort | reliability assessment of the vertical roller mill based on arima and multi observation hmm |
| topic | the vertical roller mill autoregressive integrated moving average model hidden markov model multi-observation performance reliability assessment |
| url | http://dx.doi.org/10.1080/23311916.2016.1270703 |
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