Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study

Applied Sciences

António J. Marques Cardoso¹  Alexandre Martins²  João Reis²  Inácio Fonseca³  José Torres Farinha³ 

[1] CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-62001-001 Covilhã, Portugal;EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal;ISEC/IPC—Polytechnic Institute of Coimbra, 3045-093 Coimbra, Portugal;

关键词: Hidden Markov Models;    industrial sensors;    condition-based maintenance;    big data;    cluster analysis;    principal component analysis;   

DOI  :  10.3390/app11167685

来源: DOAJ

【 摘 要 】

The availability maximization is a goal for any organization because the equipment downtime implies high non-production costs and, additionally, the abnormal stopping and restarting usually imply loss of product’s quality. In this way, a method for predicting the equipment’s health state is vital to maintain the production flow as well as to plan maintenance intervention strategies. This paper presents a maintenance prediction approach based on sensing data managed by Hidden Markov Models (HMM). To do so, a diagnosis of drying presses in a pulp industry is used as case study, which is done based on data collected every minute for three years and ten months. This paper presents an approach to manage a multivariate analysis, in this case merging the values of sensors, and optimizing the observable states to insert into a HMM model, which permits to identify three hidden states that characterize the equipment’s health state: “Proper Function”, “Alert state”, and “Equipment Failure”. The research described in this paper demonstrates how an equipment health diagnosis can be made using the HMM, through the collection of observations from various sensors, without information of machine failures occurrences. The approach developed demonstrated to be robust, even the complexity of the system, having the potential to be generalized to any other type of equipment.

【 授权许可】

Unknown