Research on Fault Signal Reconstruction of Treadmill Equipment Based on Deep Neural Network

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.