Reliability assessment of the vertical roller mill based on ARIMA and multi-observation HMM. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Wang, Qiang; Fang, Yilin; Zhou, Zude; Zuo, Jie; Xiao, Qili; Zhou, Shujuan
• Editors: Zhou, Zude
• Is Part Of: Cogent engineering. Volume 4:Issue 1(2017)
• Publication Date: 2017-01-01
• Subjects: the vertical roller mill -- autoregressive integrated moving average model -- hidden Markov model -- multi-observation -- performance reliability assessment; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2016.1270703
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: 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.