| 刘建新,杨庆玲.ABC算法优化SVR的磨损故障预测模型[J].装备环境工程,2017,14(11):98-102. LIU Jian-xin,YANG Qing-ling.Wear Fault Prediction Model Based on SVR Optimized by ABC[J].Equipment Environmental Engineering,2017,14(11):98-102. |
| ABC算法优化SVR的磨损故障预测模型 |
| Wear Fault Prediction Model Based on SVR Optimized by ABC |
| 投稿时间：2017-07-04 修订日期：2017-11-15 |
| DOI：10.7643/ issn.1672-9242.2017.11.020 |
| 中文关键词: 磨损故障 人工蜂群优化算法 支持向量回归 预测模型 |
| 英文关键词:wear faults artificial bee colony optimization algorithm support vector machine for regression prediction model |
| 基金项目:中国科学院战略性先导科技专项（A类）（XDA13040601）；国家自然科学基金（51475449）；国家973计划子课题（2014CB643302）；江苏省重点研发计划（BE2016115）；宁波市自然科学基金（2016A610267） |
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| 中文摘要: |
| 目的 为了提高故障预测的精度，针对支持向量回归SVR(Support vector machine for regression，SVR)参数选择困难的问题，提出一种采用人工蜂群 (artificial bee colony，ABC)算法优化支持向量回归（SVR）的故障预测模型(ABC-SVR)。方法 该模型先对样本数据进行重构，然后将故障预测误差（适应度）作为优化目标，通过ABC算法寻优找到最优的SVR参数，建立故障预测模型。最后通过实例仿真验证模型的优越性。结果 采用ABC算法优化的SVR故障预测模型进行时间序列预测，能够较好地跟踪发动机滑油金属元素浓度的变化过程，并且能够提前2个取样时间预测异常情况的出现。结论 ABC-SVR模型有效解决了SVR参数选择难题，能够更加准确地表现故障变化规律，提高了故障预测精度。 |
| 英文摘要: |
| 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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