Piezoelectric Active Sensor Self-Diagnosis for Electromechanical Impedance Monitoring Using K-Means Clustering Analysis and Artificial Neural Network

Abstract

Piezoelectric sensor is a crucial part of electromechanical impedance technology whose state will directly affect the effectiveness and accuracy of structural health monitoring (SHM). So carrying out sensor self-diagnosis is important and necessary. However, it is still difficult to distinguish sensor faults from structural damage as well as identify the cases and degrees of sensor faults. In the study, three characteristic indexes of admittance which have different indication intervals for damages of structure and sensors were selected from six indexes after comparison. To improve the discrimination effect, three principal components (PC) were extracted by principal component analysis (PCA). And the damage information represented by PCs was clustered by the K-means algorithm to identify the cases of damage. Then, the degrees of sensor damages were classified with the artificial neural network (ANN). The results show that the K-means clustering analysis based on admittance characteristics can accurately distinguish and identify the structural damage and four kinds of sensor damages, namely, pseudosoldering, debonding, wear, and breakage. The trained ANN model has a good recognition effect on the damage degrees and the accuracy of recognition reaches 100%. This study has a certain reference value for piezoelectric sensor self-fault identification.