A self-powered piezoelectric sensor with dual-branch CNN for timing belt health monitoring
Journal
Active and Passive Smart Structures and Integrated Systems XIX
Part Of
Active and Passive Smart Structures and Integrated Systems XIX
Start Page
48
Date Issued
2025-03-17
Author(s)
Editor(s)
Tol, Serife
Nouh, Mostafa A.
Yang, Jinkyu
Huang, Guoliang
Li, Xiaopeng
Chen, Yangyang
Sugino, Christopher
Abstract
This article introduces a novel self-powered piezoelectric sensor integrated with a convolutional neural network (CNN) for monitoring the health of timing belts during rotary operation. The sensor utilizes a two-point magnetic plucking mechanism, detecting torque-induced phase shifts between two magnets by measuring voltage variations. These phase shifts are also affected by wear on the timing belt, enabling the CNN to classify the belt's condition into three states: normal, transient, or fault, based on voltage data. However, relying solely on time-domain voltage signals makes it challenging to accurately assess the transient state. To address this, a dual-branch convolutional neural network model is proposed, designed to improve recognition accuracy even with limited data. Experimental results demonstrate that the dual-branch model significantly enhances feature extraction during the piezoelectric sensor's resonance, compared to traditional single-branch models. This leads to a more accurate distinction between transient and other states, achieving an average accuracy rate of 96%.
Event(s)
Active and Passive Smart Structures and Integrated Systems XIX 2025, Vancouver, 17 March 2025 through 21 March 2025. Code 209150
Subjects
Convolutional Neural Network (CNN)
Dual-Branch CNN
Self-Powered Piezoelectric Sensor
Timing Belt Health Monitoring
Transient State Recognition
Two-Point Magnetic Plucking
Publisher
SPIE
Type
conference paper