Research Article
Research on Fault Signal Reconstruction of Treadmill Equipment Based on Deep Neural Network
@INPROCEEDINGS{10.1007/978-3-031-50543-0_17, author={Lingling Cui and Juan Li}, title={Research on Fault Signal Reconstruction of Treadmill Equipment Based on Deep Neural Network}, proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part I}, proceedings_a={ADHIP}, year={2024}, month={3}, keywords={Deep Neural Network Community Structure Treadmill Equipment Fault Signal Reconstruction Orthogonal Matching Pursuit}, doi={10.1007/978-3-031-50543-0_17} }- Lingling Cui
Juan Li
Year: 2024
Research on Fault Signal Reconstruction of Treadmill Equipment Based on Deep Neural Network
ADHIP
Springer
DOI: 10.1007/978-3-031-50543-0_17
Abstract
There are a large number of noise components in the fault signals of treadmill equipment, which leads to increased difficulty in signal reconstruction. Therefore, a new method for reconstructing fault signals of treadmill equipment is proposed by introducing deep neural networks. Based on the community structure, fault source localization is achieved through two stages: partitioning fault areas and predicting fault propagation paths. A fault signal acquisition platform is designed based on the fault source localization results, and the collection of fault signals from the treadmill equipment is implemented. A denoising model based on a dual-layer recurrent neural network is constructed using deep neural networks to perform denoising processing on the collected fault signals. The signal reconstruction of treadmill equipment faults is completed using a matching tracking algorithm. The test results show that the reconstruction time of this method is less than 6000 ms, and the minimum signal-to-noise ratio of the reconstructed signal reaches 49.30 dB, demonstrating good practical application effects.
