Fault Detection and Diagnosis for PZT Sensors with Electro-Mechanical Impedance Technique by using One-Dimensional Convolutional Autoencoder

Piezoelectric lead zirconate titanate (PZT) sensors are widely used in various structural health monitoring (SHM) applications, where data acquired by the PZT sensors are used for damage detection. Any failure of the PZT sensors will have a detrimental effect in the ability of SHM systems to detect damage. Therefore, detecting faulty PZT sensor is critical to reduce any false-calls associated with malfunctioning sensor to ensure proper functionality of SHM systems. This paper proposes a self-diagnostic method to monitoring the health of PZT sensors using the electro-mechanical impedance (EMI) data in two steps. In the first detection step, the one-dimensional convolutional autoencoder (1D-CAE) is employed to obtain the reconstruction error as anomaly scores from the raw EMI data. Hence, the faulty PZT sensors can be detected by comparing the anomaly score with a pre-defined threshold. In the second diagnostic step, the data feature is first extracted with the 1D-CAE. The extracted feature is then fed into a multilayer perceptron (MLP) classifier to classify the fault type of the PZT sensor. The proposed method was validated through experiments, where typical in-service induced damages such as impact, environmental effect, sensor breakage localized high temperature heating, etc. were introduced. The results demonstrate the effectiveness of the proposed method for both detection and diagnosis of various types of PZT sensor damage.