Abstract
Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.
Highlights
Rolling element bearings are the core components in rotating mechanisms, whose health conditions, for example, the fault diameters in different places under different loads, could have enormous impact on the performance, stability and life span of the mechanism
The classification stage is a multi-layer perceptron, which is composed of several fully-connected local features of the input local region, which is usually referred to as weight-sharing in the literature
In recent years, Rectified Linear Unit (ReLU) was widely used as activation unit to accelerate the convergence of the CNNs
Summary
Rolling element bearings are the core components in rotating mechanisms, whose health conditions, for example, the fault diameters in different places under different loads, could have enormous impact on the performance, stability and life span of the mechanism. In many studies, the classifier was trained with a very specific type of data, which means it may achieve high accuracy on similar data while performing poorly with another type This may be caused by the wrong presentative features extracted from the raw signals. Many methods can achieve good results in fault diagnosis, few of them work directly on raw temporal signals. Convolution Neural Networks with Wide first-layer kernels (WDCNN) The contributions of this paperThird, are summarized below: few algorithms perform well under noisy environment conditions. (3) This algorithm performs well under noisy environment conditions, when working directly on (1) We propose a novel and simple learning framework, which works directly on raw temporal signals. By unsupervised learning [27]; (c) the proposed method
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