Abstract:Fault prediction and preventive maintenance are critical in the field of equipment maintenance support. This paper proposes a cross-disciplinary long-term time series-based equipment fault prediction model, focusing on the application of Long Short-Term Memory (LSTM) networks in deep learning. By processing and analyzing long-term time series data across different disciplines, an efficient predictive model was successfully constructed. Experimental analysis on the operational data from a specific equipment health monitoring system verified the model's engineering application performance. In fault prediction, the accuracy on the test set reached 95%, demonstrating high accuracy and stability. The model’s ability to identify potential faults in advance provides timely maintenance recommendations for personnel. This innovative technical solution significantly enhances fault prediction in the equipment field and offers valuable engineering applications for maintenance and support.
