ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM

Abstract

Extracting degradation features is an important part of Monitoring the health status of machinery. The performance of degradation features fluctuates or even declines with the continuous operation of the rotating machinery for a long time, which makes it difficult to extract and select degradation features. First, a feature mapping Algorithm library was used to extract features from the vibration signals and the original feature set was filtered based on Kolmogorov-smirnov（KS） test and Benjamini-Yekutieli process. Then, the optimal feature subset was searched in the supervised environment by combining BOGA with SVC. The accuracy of SVC and the dimension of subset were two objective functions of BOGA, the former was maximized, the latter was minimized. The performance of proposed method was verified by the experiment on the data set of hydraulic pump degradation state and the comparison with FRESHPCAa, ReliefF and JMIM on the case western reserve university bearing data set.

Keywords

• Rotating machinery;Degradation state identification;Bi-objective optimization genetic algorithm（BOGA）;Support vector classifier（SVC）