Thermal-Robust Mechanical Fault Diagnosis via Hybrid Convolutional Neural Network and Multisensor Fusion with a Novel Temperature Compensation Algorithm

Bolt loosening is a significant concern in structural health monitoring (SHM), and the nonlinear wave modulation (NWM) technique shows promise for its detection. However, temperature variations affect piezoelectric sensors, complicating detection. This study examines thermal stress effects on NWM-based damage detection by simulating a beam with boundary loosening under thermal stress. A temperature compensation method was developed to mitigate these effects. A multibolt structure was also analyzed using a hybrid deep learning model combining convolutional neural networks (CNNs) and Dezert-Smarandache Theory (DSmT) for multisensor data fusion. Results show that thermal stress significantly reduces classification accuracy. At 60 °C, the Damage Index dropped to zero, and CNN classification accuracy fell from 87.5% (at 25 °C) to 27.08%. Applying the compensation algorithm restored CNN accuracy to 87.27%. Statistical tests confirmed the compensated results at 60 °C were comparable to those at 25 °C. The DSmT-based fusion model achieved 98.92% accuracy on average, and even at 60 °C, maintained 98.80% accuracy with compensation for a multibolted plate. It combines the probabilistic outputs of multiple CNNs using the PCR6 rule, which redistributes conflicting evidence to improve decision reliability. This fusion strategy effectively handles uncertainty across sensors and significantly enhances classification robustness under temperature variations. In conclusion, thermal stress adversely impacts SHM damage detection, but the proposed compensation method effectively restores performance. Moreover, integrating multisensor fusion with DSmT greatly enhances accuracy, offering a robust solution for SHM in varying thermal environments.