Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems
Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety l...
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MDPI AG
2020-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/12/4120 |
| _version_ | 1797565243720728576 |
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| author | Francesca Calabrese Alberto Regattieri Lucia Botti Cristina Mora Francesco Gabriele Galizia |
| author_facet | Francesca Calabrese Alberto Regattieri Lucia Botti Cristina Mora Francesco Gabriele Galizia |
| author_sort | Francesca Calabrese |
| collection | DOAJ |
| description | Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising. |
| first_indexed | 2024-03-10T19:09:22Z |
| format | Article |
| id | doaj.art-a042f9f46442423ba166b4f047b5bc4a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:09:22Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a042f9f46442423ba166b4f047b5bc4a2023-11-20T03:54:46ZengMDPI AGApplied Sciences2076-34172020-06-011012412010.3390/app10124120Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical SystemsFrancesca Calabrese0Alberto Regattieri1Lucia Botti2Cristina Mora3Francesco Gabriele Galizia4Department of Industrial Engineering (DIN), University of Bologna, 40136 Bologna, ItalyDepartment of Industrial Engineering (DIN), University of Bologna, 40136 Bologna, ItalyInterdepartment Research Center on Security and Safety (CRIS), University of Modena and Reggio Emilia, 41121 Modena, ItalyDepartment of Industrial Engineering (DIN), University of Bologna, 40136 Bologna, ItalyDepartment of Industrial Engineering (DIN), University of Bologna, 40136 Bologna, ItalyPredictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.https://www.mdpi.com/2076-3417/10/12/4120predictive maintenancePrognostic Health Managementstreaming analysis |
| spellingShingle | Francesca Calabrese Alberto Regattieri Lucia Botti Cristina Mora Francesco Gabriele Galizia Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems Applied Sciences predictive maintenance Prognostic Health Management streaming analysis |
| title | Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems |
| title_full | Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems |
| title_fullStr | Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems |
| title_full_unstemmed | Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems |
| title_short | Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems |
| title_sort | unsupervised fault detection and prediction of remaining useful life for online prognostic health management of mechanical systems |
| topic | predictive maintenance Prognostic Health Management streaming analysis |
| url | https://www.mdpi.com/2076-3417/10/12/4120 |
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