Paper
7 August 2024 Wavelet threshold decomposition and mutation prediction method for fault signals of highway electromechanical equipment
Jingsu Luo
Author Affiliations +
Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132240Z (2024) https://doi.org/10.1117/12.3035112
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China
Abstract
Mechanical and electrical equipment on highways may affect traffic smoothness and safety after faults occur. By predicting sudden changes in fault signals, potential problems with mechanical and electrical equipment can be identified in advance, accelerating fault diagnosis and repair speed, and ensuring the normal operation of mechanical and electrical equipment on highways and road safety. Therefore, a wavelet threshold decomposition and mutation prediction method for fault signals of highway electromechanical equipment is proposed. The wavelet threshold decomposition method is introduced to denoise the fault signals of highway electromechanical equipment. Based on the singular value decomposition method, the feature vectors of the denoised fault signals of highway electromechanical equipment are extracted. On this basis, the Grey Wolf Algorithm Support Vector Machine model is used to predict sudden changes in fault signals of highway electromechanical equipment. The experimental results show that the proposed method has good performance in predicting sudden changes in fault signals of highway electromechanical equipment, and can effectively improve the efficiency of predicting sudden changes in fault signals of highway electromechanical equipment.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jingsu Luo "Wavelet threshold decomposition and mutation prediction method for fault signals of highway electromechanical equipment", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132240Z (7 August 2024); https://doi.org/10.1117/12.3035112
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KEYWORDS
Wavelets

Singular value decomposition

Feature extraction

Support vector machines

Denoising

Instrument modeling

Mathematical optimization

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