Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations
This study shows the application of a system to monitor the state of damage of railway wheel steel specimens during rolling contact fatigue tests. This system can make continuous measurements with an evaluation of damage without stopping the tests and without destructive measurements. Four tests wer...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/11/2/283 |
| _version_ | 1797413670047711232 |
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| author | Luca Provezza Ileana Bodini Candida Petrogalli Matteo Lancini Luigi Solazzi Michela Faccoli |
| author_facet | Luca Provezza Ileana Bodini Candida Petrogalli Matteo Lancini Luigi Solazzi Michela Faccoli |
| author_sort | Luca Provezza |
| collection | DOAJ |
| description | This study shows the application of a system to monitor the state of damage of railway wheel steel specimens during rolling contact fatigue tests. This system can make continuous measurements with an evaluation of damage without stopping the tests and without destructive measurements. Four tests were carried out to train the system by recording torque and vibration data. Both statistical and spectral features were extracted from the sensors signals. A Principal Component Analysis (PCA) was performed to reduce the volume of the initial dataset; then, the data were classified with the k-means algorithm. The results were then converted into probabilities curves. Metallurgical investigations (optical micrographs, wear curves) and hardness tests were carried out to assess the trends of machine learning analysis. The training tests were used to train the proposed algorithm. Three validation tests were performed by using the real-time results of the k-means algorithm as a stop condition. Metallurgical analysis was performed also in this case. The validation tests follow the results of the training test and metallurgical analysis confirms the damage found with the machine learning analysis: when the membership probability of the cluster corresponding to the damage state reaches a value higher than 0.5, the metallurgical analysis clearly shows the cracks on the surface of the specimen due to the rolling contact fatigue (RCF) damage mechanism. These preliminary results are positive, even if reproduced on a limited set of specimens. This approach could be integrated in rolling contact fatigue tests to provide additional information on damage progression. |
| first_indexed | 2024-03-09T05:21:17Z |
| format | Article |
| id | doaj.art-78a795bdef5e4f68902e02adb32a0b8e |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T05:21:17Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-78a795bdef5e4f68902e02adb32a0b8e2023-12-03T12:40:21ZengMDPI AGMetals2075-47012021-02-0111228310.3390/met11020283Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and VibrationsLuca Provezza0Ileana Bodini1Candida Petrogalli2Matteo Lancini3Luigi Solazzi4Michela Faccoli5Department of Industrial and Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Industrial and Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Industrial and Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Industrial and Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Industrial and Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyDepartment of Industrial and Mechanical Engineering, University of Brescia, Via Branze 38, 25123 Brescia, ItalyThis study shows the application of a system to monitor the state of damage of railway wheel steel specimens during rolling contact fatigue tests. This system can make continuous measurements with an evaluation of damage without stopping the tests and without destructive measurements. Four tests were carried out to train the system by recording torque and vibration data. Both statistical and spectral features were extracted from the sensors signals. A Principal Component Analysis (PCA) was performed to reduce the volume of the initial dataset; then, the data were classified with the k-means algorithm. The results were then converted into probabilities curves. Metallurgical investigations (optical micrographs, wear curves) and hardness tests were carried out to assess the trends of machine learning analysis. The training tests were used to train the proposed algorithm. Three validation tests were performed by using the real-time results of the k-means algorithm as a stop condition. Metallurgical analysis was performed also in this case. The validation tests follow the results of the training test and metallurgical analysis confirms the damage found with the machine learning analysis: when the membership probability of the cluster corresponding to the damage state reaches a value higher than 0.5, the metallurgical analysis clearly shows the cracks on the surface of the specimen due to the rolling contact fatigue (RCF) damage mechanism. These preliminary results are positive, even if reproduced on a limited set of specimens. This approach could be integrated in rolling contact fatigue tests to provide additional information on damage progression.https://www.mdpi.com/2075-4701/11/2/283machine learningrolling contact fatigue testsdamage assessmentmetallurgical analysis |
| spellingShingle | Luca Provezza Ileana Bodini Candida Petrogalli Matteo Lancini Luigi Solazzi Michela Faccoli Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations Metals machine learning rolling contact fatigue tests damage assessment metallurgical analysis |
| title | Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations |
| title_full | Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations |
| title_fullStr | Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations |
| title_full_unstemmed | Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations |
| title_short | Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations |
| title_sort | monitoring the damage evolution in rolling contact fatigue tests using machine learning and vibrations |
| topic | machine learning rolling contact fatigue tests damage assessment metallurgical analysis |
| url | https://www.mdpi.com/2075-4701/11/2/283 |
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