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Volume 15
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10.3390/app15031326
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Haoxiang Yu
1,
Libin Wang
2,
Weiquan Xu
3,
Siya Mi
4,5 and
Yu Zhang
1,*
1
The Key Lab of Computer Network and Information Integration (Ministry of Education), School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
2
Hangzhou Special Equipment Inspection and Science Research Institute, Hangzhou 310015, China
3
Nanjing U-lake Technology Co., Ltd., Nanjing 211111, China
4
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
5
Purple Mountain Laboratories, Nanjing 211189, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(3), 1326; https://doi.org/10.3390/app15031326
Submission received: 8 November 2024 / Revised: 22 January 2025 / Accepted: 25 January 2025 / Published: 27 January 2025
Versions Notes

Abstract

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.
Keywords: time series forecasting; elevator fault prediction; transformer time series forecasting; elevator fault prediction; transformer

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MDPI and ACS Style

Yu, H.; Wang, L.; Xu, W.; Mi, S.; Zhang, Y. RPM-ETC: A Risk Prediction Model for Elevators Based on Transformer and Self-Temporal Compression Mechanism. Appl. Sci. 2025, 15, 1326. https://doi.org/10.3390/app15031326

AMA Style

Yu H, Wang L, Xu W, Mi S, Zhang Y. RPM-ETC: A Risk Prediction Model for Elevators Based on Transformer and Self-Temporal Compression Mechanism. Applied Sciences. 2025; 15(3):1326. https://doi.org/10.3390/app15031326

Chicago/Turabian Style

Yu, Haoxiang, Libin Wang, Weiquan Xu, Siya Mi, and Yu Zhang. 2025. "RPM-ETC: A Risk Prediction Model for Elevators Based on Transformer and Self-Temporal Compression Mechanism" Applied Sciences 15, no. 3: 1326. https://doi.org/10.3390/app15031326

APA Style

Yu, H., Wang, L., Xu, W., Mi, S., & Zhang, Y. (2025). RPM-ETC: A Risk Prediction Model for Elevators Based on Transformer and Self-Temporal Compression Mechanism. Applied Sciences, 15(3), 1326. https://doi.org/10.3390/app15031326

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