Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data
Abstract It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, ac...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-02-01
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| Series: | Friction |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40544-023-0834-7 |
| _version_ | 1797219634456297472 |
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| author | Robert Gutierrez Tianshi Fang Robert Mainwaring Tom Reddyhoff |
| author_facet | Robert Gutierrez Tianshi Fang Robert Mainwaring Tom Reddyhoff |
| author_sort | Robert Gutierrez |
| collection | DOAJ |
| description | Abstract It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, acoustic data from sliding contacts is notoriously complex and difficult to interpret. Herein, we simultaneously measure coefficient of friction (with a conventional force transducer) and acoustic emission (with a piezoelectric sensor and high acquisition rate digitizer) produced by a steel–steel rubbing contact. Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this (e.g., down-sampling, averaging, fast Fourier transforms (FFTs), histograms). Next, fresh, unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction. There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data, with root mean square (RMS) errors as low as 0.03 and Pearson correlation coefficients reaching 0.8. Moreover, predictions remain accurate despite changes in test conditions such as normal load, reciprocating frequency, and stroke length. This paves the way for remote, acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance. |
| first_indexed | 2024-04-24T12:36:46Z |
| format | Article |
| id | doaj.art-e9a0ef9b56e74119bc23c91e1b2fdc1e |
| institution | Directory Open Access Journal |
| issn | 2223-7690 2223-7704 |
| language | English |
| last_indexed | 2024-04-24T12:36:46Z |
| publishDate | 2024-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Friction |
| spelling | doaj.art-e9a0ef9b56e74119bc23c91e1b2fdc1e2024-04-07T11:30:30ZengSpringerOpenFriction2223-76902223-77042024-02-011261299132110.1007/s40544-023-0834-7Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission dataRobert Gutierrez0Tianshi Fang1Robert Mainwaring2Tom Reddyhoff3Tribology Group, Department of Mechanical Engineering, Imperial College LondonShell Global Solutions (US) Inc. Shell Technology Center HoustonShell International Petroleum Company Limited, Shell CentreTribology Group, Department of Mechanical Engineering, Imperial College LondonAbstract It is increasingly important to monitor sliding interfaces within machines, since this is where both energy is lost, and failures occur. Acoustic emission (AE) techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials. However, acoustic data from sliding contacts is notoriously complex and difficult to interpret. Herein, we simultaneously measure coefficient of friction (with a conventional force transducer) and acoustic emission (with a piezoelectric sensor and high acquisition rate digitizer) produced by a steel–steel rubbing contact. Acquired data is then used to train machine learning (ML) algorithms (e.g., Gaussian process regression (GPR) and support vector machine (SVM)) to correlated acoustic emission with friction. ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this (e.g., down-sampling, averaging, fast Fourier transforms (FFTs), histograms). Next, fresh, unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction. There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data, with root mean square (RMS) errors as low as 0.03 and Pearson correlation coefficients reaching 0.8. Moreover, predictions remain accurate despite changes in test conditions such as normal load, reciprocating frequency, and stroke length. This paves the way for remote, acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.https://doi.org/10.1007/s40544-023-0834-7acoustic emissioncondition monitoringfrictionmachine learningGaussian process regressionsupport vector machine |
| spellingShingle | Robert Gutierrez Tianshi Fang Robert Mainwaring Tom Reddyhoff Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data Friction acoustic emission condition monitoring friction machine learning Gaussian process regression support vector machine |
| title | Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data |
| title_full | Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data |
| title_fullStr | Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data |
| title_full_unstemmed | Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data |
| title_short | Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data |
| title_sort | predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data |
| topic | acoustic emission condition monitoring friction machine learning Gaussian process regression support vector machine |
| url | https://doi.org/10.1007/s40544-023-0834-7 |
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