Abstract
Data-driven fault diagnosis is critical for the rolling bearing to improve its healthy states and save invaluable cost. Nowadays, various intelligent fault diagnosis methods using machine learning (ML) or deep learning (DL) techniques have achieved much success. The convolutional neural network (CNN) based method, as a representative DL technique, can extract the features of raw data automatically for its excellent sparse connectivity and weight sharing properties. In this paper, a novel data-driven intelligent fault diagnosis method of rolling bearing under variable working loads has been proposed by using 1-D stacked dilated convolutional neural network (1D-SDCNN). First, 1-D vibration signals were used as input data without additional signal processing and diagnostic expertise. Second, the stacked dilated convolution, which can capture larger scale associated information and achieve large receptive fields with a few layers, was used to replace the traditional convolution and pooling structure. Third, the 1D-SDCNN architecture was flexible which is based on the relationship between receptive fields and the length of the input signal. And the number of network layers can be adjusted according to signal length. Further, it can adapt to the changing working loads of the mechanical environment. Finally, the effectiveness of the proposed method was confirmed through the experiment. And the results demonstrated that 1D-SDCNN was able to learn in-deep features under three variable working loads and the average accuracy was 96.8%.
Highlights
The rolling bearing is the critical part of the rotating machinery such as rolling mills, wind turbines, and its health conditions are significant which can affect the working stability and reliability of rotating machinery
EXPERIMENT STEPS 1) EXPERIMENT 1: DIAGNOSIS RESULTS UNDER THE SAME LOAD we evaluate the performance of the proposed model for the data classification under the same load
The novelty is the structure of the fault diagnosis model is related to the length of the input signal and the receptive fields
Summary
The rolling bearing is the critical part of the rotating machinery such as rolling mills, wind turbines, and its health conditions are significant which can affect the working stability and reliability of rotating machinery. The wide kernels in the first layer can capture the positional relationship between features from a long vibration signal, and the following layers are stacked by small convolution kernels, that made the networks deeper than the above CNN methods It was robust when working under the environments of load changes and noise. A dilated convolution effectively allows the network to operate on a coarser scale than with a standard convolution, and the receptive fields of stacked dilated convolutions are growing exponentially without max-pooling operations, complex calculation and parameters increasing Motivated by these advantages, a novel 1-D stacked dilated convolutional neural network (1D-SDCNN) has been proposed and applied in data-driven intelligent fault diagnosis of rolling bearing under variable working loads. K =1 where pi(x) denotes the real probability and qi(x) is the estimated probability that comes from Equation 10
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