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Digital twin-driven machine learning: ball bearings fault severity classification
| Content Provider | Scilit |
|---|---|
| Author | Farhat, Mohamed Habib Chiementin, Xavier Chaari, Fakher Bolaers, Fabrice Haddar, Mohamed |
| Copyright Year | 2020 |
| Description | Journal: Measurement Science and Technology Machine learning algorithms (MLAs) are increasingly being used as effective techniques for processing vibration signals obtained from complex industrial machineries. Previous applications of automatic fault detection algorithms in the diagnosis of rotating machines were mainly based on historical operating data sets, limiting the diagnostic reliability to devices with an extended operating history. Moreover, physically collected data are often restricted by the conditions of acquisition and the specific elements for which they were recorded. Digital twin (DT) provides a powerful tool able to generate a huge amount of training data for MLAs. However, the DT model must be accurate enough to substitute the experiments. This work aims to escape the experience requirement by using a simulation-driven MLA based on the multifactorial analysis of fault indicators associated with a DT. To achieve this approach, a numerical model of a rotor-ball bearing system is developed. The latter is updated according to a parameter update scheme based on a comparison between the relevant features of the experimentally measured signals and the signals simulated by the model. These features are chosen as the selected input parameters of the MLA classifier. The results show that after updating, the developed DT has provided a reliable diagnostic with an adaptive degradation analysis, which makes the simulated data suitable for the construction of a machine learning predictive model. Two common MLAs, (multi-kernel support vector machine) and (k nearest neighbor’s algorithm), were trained using the simulated data and validated later against experimental datasets. |
| Related Links | https://iopscience.iop.org/article/10.1088/1361-6501/abd280/pdf |
| ISSN | 09570233 |
| e-ISSN | 13616501 |
| DOI | 10.1088/1361-6501/abd280 |
| Journal | Measurement Science and Technology |
| Issue Number | 4 |
| Volume Number | 32 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2021-04-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Measurement Science and Technology Mechanical Engineering Diagnostic Reliability Using A Simulation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Instrumentation Engineering |
