Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation
We propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Signals |
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| Online Access: | https://www.mdpi.com/2624-6120/2/4/40 |
| _version_ | 1797500559856500736 |
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| author | Jun Sun Qiao Sun |
| author_facet | Jun Sun Qiao Sun |
| author_sort | Jun Sun |
| collection | DOAJ |
| description | We propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are used in our study. The challenges with the datasets include a very limited number of run-to-failure examples, no failure mode information, and a wide range of bearing life spans. Without a large number of training samples, feature engineering is necessary. Principal component analysis is applied to the spectrogram of vibration signals to obtain prognostic feature sequences. A data augmentation strategy is developed to generate synthetic prognostic feature sequences using learning instances. Subsequently, similarities between the test and learning instances can be assessed using a root mean squared (RMS) difference measure. Finally, an ensemble method is developed to aggregate the RUL estimates based on multiple similar prognostic feature sequences. The proposed approach demonstrates comparable performance with published solutions in the literature. It serves as an alternative method for solving the RUL estimation problem. |
| first_indexed | 2024-03-10T03:05:34Z |
| format | Article |
| id | doaj.art-353b3c1dba5643d0834b1e94bd94d159 |
| institution | Directory Open Access Journal |
| issn | 2624-6120 |
| language | English |
| last_indexed | 2024-03-10T03:05:34Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Signals |
| spelling | doaj.art-353b3c1dba5643d0834b1e94bd94d1592023-11-23T10:33:00ZengMDPI AGSignals2624-61202021-10-012466268710.3390/signals2040040Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity EvaluationJun Sun0Qiao Sun1Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaDepartment of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, CanadaWe propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are used in our study. The challenges with the datasets include a very limited number of run-to-failure examples, no failure mode information, and a wide range of bearing life spans. Without a large number of training samples, feature engineering is necessary. Principal component analysis is applied to the spectrogram of vibration signals to obtain prognostic feature sequences. A data augmentation strategy is developed to generate synthetic prognostic feature sequences using learning instances. Subsequently, similarities between the test and learning instances can be assessed using a root mean squared (RMS) difference measure. Finally, an ensemble method is developed to aggregate the RUL estimates based on multiple similar prognostic feature sequences. The proposed approach demonstrates comparable performance with published solutions in the literature. It serves as an alternative method for solving the RUL estimation problem.https://www.mdpi.com/2624-6120/2/4/40bearing faultsremaining useful lifeprognosticsinstance-based learningdata augmentationspectrogram |
| spellingShingle | Jun Sun Qiao Sun Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation Signals bearing faults remaining useful life prognostics instance-based learning data augmentation spectrogram |
| title | Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation |
| title_full | Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation |
| title_fullStr | Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation |
| title_full_unstemmed | Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation |
| title_short | Bearing Prognostics: An Instance-Based Learning Approach with Feature Engineering, Data Augmentation, and Similarity Evaluation |
| title_sort | bearing prognostics an instance based learning approach with feature engineering data augmentation and similarity evaluation |
| topic | bearing faults remaining useful life prognostics instance-based learning data augmentation spectrogram |
| url | https://www.mdpi.com/2624-6120/2/4/40 |
| work_keys_str_mv | AT junsun bearingprognosticsaninstancebasedlearningapproachwithfeatureengineeringdataaugmentationandsimilarityevaluation AT qiaosun bearingprognosticsaninstancebasedlearningapproachwithfeatureengineeringdataaugmentationandsimilarityevaluation |