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Efficient sub-pixel fully connected neural network: an intelligent fault diagnosis method for signal resolution enhancement

Citation

Wang, Xiaoyu et al. (2021), Efficient sub-pixel fully connected neural network: an intelligent fault diagnosis method for signal resolution enhancement, Dryad, Dataset, https://doi.org/10.5061/dryad.37pvmcvhm

Abstract

Using deep learning to augment the dataset has become a hot topic in various fields. That is, to generate a more simulated dataset from the limited dataset. However, the premise of the study is to use high resolution sampling equipment to collect experimental data. In this paper, we propose a simple and effective algorithm -- efficient sub-pixel fully connected neural network (ESPFCN). Different from other data enhance algorithms, ESPFCN does not explicitly generate more simulation data. On the contrary, it performs the fully-connected operation on the original input data and outputs the results of four-channel multi-feature maps. Through the sub-pixel fully connected layer, the data resolution is changed from low to high and increased to 4 times of the original. Finally, two set of bearing and gearbox experiments are set up to evaluate the performance of the generated model. The experimental results verify the effectiveness of the ESPFCN model, and the feature learning process of ESPFCN is visualized.

Methods

The bearing vibration signal is collected by LMS data acquisition instrument with a vibration sensor, and the senor is placed on the side of the bearing seat. The sampling frequency is 25.6 kHz. The engine speed is 3000 r/min and the bearing type is N205EU cylindrical roller bearing. As is displayed in Table 1, the type of bearing has four health conditions: normal condition (NC), inner ring fault (IF), outer ring fault (OF) and roller ball fault (RF). There are three different degrees of damages for each fault type: 0.2 mm, 0.4 mm and 0.6 mm. Bearing health conditions is divided into ten kinds, each state contains 200 samples.   

The gearbox vibration signal is also collected by LMS data acquisition instrument with a vibration sensor, the motor speed is constant at 2000 r/min, and 7 kinds of gearbox signals with different health conditions are collected at different sampling frequencies (6.4 kHz, 25.6 kHz), respectively, as follows: NC (normal condition), PC (pinion crack), PP (pinion pit), WC (sun wheel crack), WCPC (sun wheel crack and pinion pit), WPPC (wheel pit and pinion crack), WPPP (sun wheel pit and pinion pit )

Funding

China Postdoctoral Science Foundation, Award: 2019M662399