Spatiotemporal Thermal Fault Prediction for Electromechanical Equipment Using High-density Sensor Arrays and Attention-enhanced Algorithm

The stable operation of electromechanical equipment is vital for modern infrastructure.However, thermal faults remain a leading cause of unexpected shutdowns.Traditional single-point monitoring and infrared (IR) inspections often fail to detect incipient, localized anomalies owing to limited timeliness and spatial resolution.Therefore, we developed a predictive system based on high-density temperature sensor arrays and advanced spatiotemporal modeling.In the system, an optimized 64-channel Class A PT100 resistance temperature detector topology with MAX31865 converters was deployed to capture dynamic thermal fields.A complete preprocessing pipeline, including denoising and feature engineering, ensured data integrity by suppressing stochastic hardware noise.The prediction model was established by integrating convolutional long short-term memory (ConvLSTM) with a convolutional block attention module (CBAM), focusing on critical spatial regions.Quantile regression analysis was used for probabilistic fault estimation and a multi-level warning system.The validation results of the system performance on a 75 kW pump for three months demonstrated that the ConvLSTM + CBAM model showed a mean absolute error of 0.50 °C, a 23% improvement over the ConvLSTM model.The model extended the average fault warning lead time to 52 min, representing a 150% increase compared with that of the LSTM model.These results show that dense sensor arrays combined with attention-based deep learning deliver accurate thermal diagnostics comparable to IR imaging, offering a scalable solution for proactive industrial maintenance.