Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery
Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/20/9580 |
| _version_ | 1797515340143394816 |
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| author | Francesca Calabrese Alberto Regattieri Marco Bortolini Francesco Gabriele Galizia Lorenzo Visentini |
| author_facet | Francesca Calabrese Alberto Regattieri Marco Bortolini Francesco Gabriele Galizia Lorenzo Visentini |
| author_sort | Francesca Calabrese |
| collection | DOAJ |
| description | Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time. |
| first_indexed | 2024-03-10T06:44:07Z |
| format | Article |
| id | doaj.art-285a91b680614feea64dbb0a2843c777 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:44:07Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-285a91b680614feea64dbb0a2843c7772023-11-22T17:20:56ZengMDPI AGApplied Sciences2076-34172021-10-011120958010.3390/app11209580Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial MachineryFrancesca Calabrese0Alberto Regattieri1Marco Bortolini2Francesco Gabriele Galizia3Lorenzo Visentini4Department of Industrial Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyDepartment of Industrial Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyDepartment of Industrial Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyDepartment of Industrial Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyDepartment of Industrial Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, ItalyGiven the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time.https://www.mdpi.com/2076-3417/11/20/9580fault diagnosisnovelty detectioncondition monitoringindustrial machine |
| spellingShingle | Francesca Calabrese Alberto Regattieri Marco Bortolini Francesco Gabriele Galizia Lorenzo Visentini Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery Applied Sciences fault diagnosis novelty detection condition monitoring industrial machine |
| title | Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery |
| title_full | Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery |
| title_fullStr | Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery |
| title_full_unstemmed | Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery |
| title_short | Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery |
| title_sort | feature based multi class classification and novelty detection for fault diagnosis of industrial machinery |
| topic | fault diagnosis novelty detection condition monitoring industrial machine |
| url | https://www.mdpi.com/2076-3417/11/20/9580 |
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