Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning
Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/1005 |
| _version_ | 1827622005047820288 |
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| author | Udeme Ibanga Inyang Ivan Petrunin Ian Jennions |
| author_facet | Udeme Ibanga Inyang Ivan Petrunin Ian Jennions |
| author_sort | Udeme Ibanga Inyang |
| collection | DOAJ |
| description | Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining. |
| first_indexed | 2024-03-09T11:16:08Z |
| format | Article |
| id | doaj.art-a5877544cea44bfca347beb31d6ee970 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:16:08Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a5877544cea44bfca347beb31d6ee9702023-12-01T00:31:10ZengMDPI AGSensors1424-82202023-01-01232100510.3390/s23021005Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble LearningUdeme Ibanga Inyang0Ivan Petrunin1Ian Jennions2Integrated Vehicle Health Management Centre, Cranfield University, Cranfield MK43 0AL, UKCentre for Autonomous and Cyberphysical Systems, Cranfield University, Cranfield MK43 0AL, UKIntegrated Vehicle Health Management Centre, Cranfield University, Cranfield MK43 0AL, UKFault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.https://www.mdpi.com/1424-8220/23/2/1005comprehensivemultiple faultsgearbearingshaftoptimization |
| spellingShingle | Udeme Ibanga Inyang Ivan Petrunin Ian Jennions Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning Sensors comprehensive multiple faults gear bearing shaft optimization |
| title | Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning |
| title_full | Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning |
| title_fullStr | Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning |
| title_full_unstemmed | Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning |
| title_short | Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning |
| title_sort | diagnosis of multiple faults in rotating machinery using ensemble learning |
| topic | comprehensive multiple faults gear bearing shaft optimization |
| url | https://www.mdpi.com/1424-8220/23/2/1005 |
| work_keys_str_mv | AT udemeibangainyang diagnosisofmultiplefaultsinrotatingmachineryusingensemblelearning AT ivanpetrunin diagnosisofmultiplefaultsinrotatingmachineryusingensemblelearning AT ianjennions diagnosisofmultiplefaultsinrotatingmachineryusingensemblelearning |