Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/16/7685 |
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| author | Alexandre Martins Inácio Fonseca José Torres Farinha João Reis António J. Marques Cardoso |
| author_facet | Alexandre Martins Inácio Fonseca José Torres Farinha João Reis António J. Marques Cardoso |
| author_sort | Alexandre Martins |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:01:41Z |
| format | Article |
| id | doaj.art-bd1ba25a25e246e5b4dde802bc98ac3c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:01:41Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bd1ba25a25e246e5b4dde802bc98ac3c2023-11-22T06:45:23ZengMDPI AGApplied Sciences2076-34172021-08-011116768510.3390/app11167685Maintenance Prediction through Sensing Using Hidden Markov Models—A Case StudyAlexandre Martins0Inácio Fonseca1José Torres Farinha2João Reis3António J. Marques Cardoso4EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, PortugalISEC/IPC—Polytechnic Institute of Coimbra, 3045-093 Coimbra, PortugalISEC/IPC—Polytechnic Institute of Coimbra, 3045-093 Coimbra, PortugalEIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, PortugalCISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-62001-001 Covilhã, PortugalThe 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.https://www.mdpi.com/2076-3417/11/16/7685Hidden Markov Modelsindustrial sensorscondition-based maintenancebig datacluster analysisprincipal component analysis |
| spellingShingle | Alexandre Martins Inácio Fonseca José Torres Farinha João Reis António J. Marques Cardoso Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study Applied Sciences Hidden Markov Models industrial sensors condition-based maintenance big data cluster analysis principal component analysis |
| title | Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
| title_full | Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
| title_fullStr | Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
| title_full_unstemmed | Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
| title_short | Maintenance Prediction through Sensing Using Hidden Markov Models—A Case Study |
| title_sort | maintenance prediction through sensing using hidden markov models a case study |
| topic | Hidden Markov Models industrial sensors condition-based maintenance big data cluster analysis principal component analysis |
| url | https://www.mdpi.com/2076-3417/11/16/7685 |
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