发动机异常检测多目标优化方法

Multi-objective Optimization of Diesel Engine Abnormal Detection

针对柴油发动机异常检测中的特征选择和分类器参数与检测精度之间的耦合关系,提出了一种基于非支配排序粒子群优化的柴油发动机异常检测封装式多目标同步优化方法。利用双树复小波包的分解与重构,对发动机振动信号进行时域、频域和时频域多角度特征提取,构建了较完备的特征参数集,分析了故障诊断中特征选择与分类器参数优化对检测精度的影响,运用非支配排序粒子群优化算法对多个优化目标进行协调和折衷处理,同时追求特征参数子集维数最小化和分类正确率最大化。实验数据分析表明,该方法能够寻找出最优的特征子集和分类器参数,提高柴油发动机异常检测的精度和效率。

The coupling problem of the features selection and classifier parameters with detection accuracy of diesel engine abnormal detection is analyzed,an abnormal detection multi-objective optimization method based on non-dominated sorting Particle Swarm Optimization is proposed.The multi-aspects feature extraction including time domain,frequency domain and time-frequency domain of diesel engine vibration signal based on dual-tree complex wavelet packet transform is studied.After analysis the detection accuracy influences of features selection and classifier parameters,Non-dominated Particle Swarm Optimization algorithm is used for feature set and classifier parameters optimization.The test vibration signal analysis results illustrate that the proposed method could find the optimal feature set and classifier parameters with minimum dimensions and maximum classification accuracy,and improve the abnormal detection accuracy and efficiency.