RPM-ETC: A Risk Prediction Model for Elevators Based on Transformer and Self-Temporal Compression Mechanism

Elevators play an indispensable role in modern urban life. Ensuring the safe operation of elevators is crucial due to the severe consequences of malfunctions. Traditional maintenance methods are costly and may not comprehensively capture potential faults. Leveraging deep learning technologies, this study proposes a Risk Prediction Model based on Elevator Transformer and Self-temporal Compression Mechanism (RPM-ETC). By analyzing rich operational data, the model predicts potential faults before significant issues arise. The model utilizes the Transformer architecture to effectively capture temporal relationships and employs a temporal compression mechanism to enhance prediction efficiency. Additionally, it uses Enhanced Positional Encoding to prevent the the loss of temporal information as network depth increases. Based on the obtained performance results, the model achieves an accuracy of 86.3% and a frame-per-second (FPS) rate of 388.7, accurately and rapidly predicting elevator faults. Additionally, this paper provides a comprehensive dataset for elevator operation prediction to facilitate further research.