Degradation state recognition of ultrasonic motor based upon locality preserving projection and support vector machine optimized by fruit fly optimization algorithm

The cracking of piezoelectric ceramic components is one of the main failure pattern of ultrasonic motors. The degradation characteristics can be extracted effectively by monitoring the voltage signal of piezoelectric sensor. High dimensional feature vector contains a lot of redundant information due to dimension disaster. Locality preserving projection (LPP) can effectively reduce dimension on the degradation feature, which can fuse the high dimension feature. The parameters of support vector machine (SVM) have an important influence on the generalization ability of the model. Fruit fly optimization algorithm (FOA) has the advantages of few parameters, fast calculation speed, strong ability of global optimization and easy to implement. FOA is utilized to optimize SVM in this paper accordingly, and the optimized SVM by FOA (FOASvM) is applied in degradation state recognition of ultrasonic motor. Finally, the effectiveness of the proposed method is verified through the comparative analysis.