Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods
This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/13/5875 |
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| author | Bartłomiej Ambrożkiewicz Arkadiusz Syta Anthimos Georgiadis Alexander Gassner Grzegorz Litak Nicolas Meier |
| author_facet | Bartłomiej Ambrożkiewicz Arkadiusz Syta Anthimos Georgiadis Alexander Gassner Grzegorz Litak Nicolas Meier |
| author_sort | Bartłomiej Ambrożkiewicz |
| collection | DOAJ |
| description | This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the bearing’s dynamics. In this article, we show the original approach to its intelligent detection through the analysis of short-time intervals and the calculation of chosen indicators, which can be assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried out based on the time series of vibrations recorded by the accelerometer and then digitally processed. Window statistical indicators widely used in the diagnosis of rolling bearings, which served as features for the machine learning models, were calculated. The accuracy of the classification turned out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series processing, which allows for the extraction of subsequent dominant frequencies into experimental modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the modes allowed for an increase in classification accuracy to over 90%. |
| first_indexed | 2024-03-11T01:29:14Z |
| format | Article |
| id | doaj.art-47f327f6fb6f4c17b0d73880be2ca686 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:29:14Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-47f327f6fb6f4c17b0d73880be2ca6862023-11-18T17:28:05ZengMDPI AGSensors1424-82202023-06-012313587510.3390/s23135875Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning MethodsBartłomiej Ambrożkiewicz0Arkadiusz Syta1Anthimos Georgiadis2Alexander Gassner3Grzegorz Litak4Nicolas Meier5Department of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, PolandDepartment of Computerization and Robotization of Production, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, PolandInstitute of Production Techniques and Systems, Leuphana University of Lüneburg, Universitätsallee 1, 21335 Lüneburg, GermanyInstitute of Production Techniques and Systems, Leuphana University of Lüneburg, Universitätsallee 1, 21335 Lüneburg, GermanyDepartment of Automation, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, PolandInstitute of Production Techniques and Systems, Leuphana University of Lüneburg, Universitätsallee 1, 21335 Lüneburg, GermanyThis article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the bearing’s dynamics. In this article, we show the original approach to its intelligent detection through the analysis of short-time intervals and the calculation of chosen indicators, which can be assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried out based on the time series of vibrations recorded by the accelerometer and then digitally processed. Window statistical indicators widely used in the diagnosis of rolling bearings, which served as features for the machine learning models, were calculated. The accuracy of the classification turned out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series processing, which allows for the extraction of subsequent dominant frequencies into experimental modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the modes allowed for an increase in classification accuracy to over 90%.https://www.mdpi.com/1424-8220/23/13/5875ball bearingsradial internal clearancetime-series analysismachine learning |
| spellingShingle | Bartłomiej Ambrożkiewicz Arkadiusz Syta Anthimos Georgiadis Alexander Gassner Grzegorz Litak Nicolas Meier Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods Sensors ball bearings radial internal clearance time-series analysis machine learning |
| title | Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods |
| title_full | Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods |
| title_fullStr | Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods |
| title_full_unstemmed | Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods |
| title_short | Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods |
| title_sort | intelligent diagnostics of radial internal clearance in ball bearings with machine learning methods |
| topic | ball bearings radial internal clearance time-series analysis machine learning |
| url | https://www.mdpi.com/1424-8220/23/13/5875 |
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