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| Wear Fault Prediction Model Based on SVR Optimized by ABC |
| Received:July 04, 2017 Revised:November 15, 2017 |
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| DOI：10.7643/ issn.1672-9242.2017.11.020 |
| KeyWord:wear faults artificial bee colony optimization algorithm support vector machine for regression prediction model |
| Author | Institution |
| LIU Jian-xin |
Yantai Engineering & Technology College, Yantai , China |
| YANG Qing-ling |
Yantai Vocational College, Yantai , China |
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| Abstract: |
| Objective To improve the prediction accuracy of wear faults, a wear fault prediction model (ABC-SVR), which was based on support vector machine for regression (SVR) optimized by artificial bee colony(ABC) algorithm was proposed. Methods The model reconstructed the time series of wear faults and took the wear fault prediction accuracy as the optimization objective to find out the optimal SVR parameters by ABC algorithm and build prediction model of wear faults. Finally, the simulative contrasting experiment was applied to test the performance of the model. Results Time series prediction with SVR forecasting model optimized by ABC algorithm could track the concentration change process of metallic element in engine lubricating oil and predict the presence of the abnormal situation ahead of 2 sampling time. Conclusion ABC-SVR solves the problem of SVR parameter optimization, can describe the complicated change rules of wear faults accurately, and improves the accuracy of wear faults prediction. |
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