Spatial condition monitoring in rotating machinery using LC-tuned piezoelectric arrays and multi-task CNN

Abstract This article introduces an innovative approach for condition monitoring of rotating instruments using a piezoelectric sensor array and a multi-task convolutional neural network (CNN) framework. The method adopts a multi-input-single-output (MISO) diagnostic strategy to detect faults in bearings and gearboxes, even in the presence of multiple defects across different locations. The sensor array, composed of piezoelectric patches arranged in a mixed parallel-series configuration, enables spatially distributed monitoring through a single output voltage signal. To enhance voltage sensing, inductors are integrated with the array to form LC resonant circuits. A CNN classifier is initially designed to identify 12 combined health states, comprising four bearing states and three gear states, and achieves high accuracy under continuous machine operation. However, the model exhibits vulnerability to noise during machine restarts and struggles with data sparsity as the number of defect combinations increases. To address these limitations, a multi-task CNN architecture is proposed. It shares convolutional layers while employing separate fully connected layers for gear and bearing classification. This structure simplifies the classification task and improves generalization for spatial condition monitoring. Experiments show that multi-task learning achieves an overall accuracy of 93%, with at least 84% accuracy in the worst conditions, outperforming the 49% worst-case accuracy of single-task learning. This demonstrates the effectiveness of multi-task learning in multi-location defect monitoring using piezoelectric arrays.