A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions

Abstract

Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the residual network. In the SRDCNN model, the dilated convolution is used to exponentially increase the receptive field of convolution kernel and extract features from the sample with more points, alleviating the influence of randomness. The input gate structure of LSTM could effectively remove noise and control the entry of information contained in the input sample. Meanwhile, the residual network is introduced to overcome the problem of vanishing gradients caused by the deeper structure of the neural network, hence improving the overall classification accuracy. The experimental results indicate that compared with three excellent models, the proposed SRDCNN model has higher denoising ability and better workload adaptability.

A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

“…To validate the effectiveness of the novel dual-path recurrent neural network with wide first kernel and deep convolutional pathway proposed in this work for intelligent data-driven fault diagnosis applications, the benchmark bearing fault dataset from Case Western Reserve University (CWRU) Bearing Data Center [ 46 ] is used. The CWRU bearing fault dataset has been widely used in the literature for investigating both conventional fault diagnosis techniques [ 47 , 48 ] and data-driven fault diagnosis methods using machine learning [ 49 , 50 ] and deep learning [ 35 , 36 , 37 ]. The data used in this study were collected from both the drive end accelerometer (close to the fault location) and the fan end accelerometer (remote from the fault location) of the test apparatus shown in Figure 5 .…”

“…Zhang et al [ 35 ] proposed a deep one-dimensional CNN with a wide first layer kernel (WDCNN) capable of working directly on the raw temporal signals without the need for complex preprocessing. Zhuang et al [ 36 ] also proposed a 1D CNN capable of operating on raw vibration signals but used dilated convolutions and residual connections to improve robustness to noise and domain shifts at the cost of some complexity in tuning the model parameters. Zhang et al [ 37 ] adapted the WDCNN proposed in [ 35 ] to work with limited data—a common challenge in fault diagnosis where components are rarely allowed to run to failure.…”

Fault Diagnosis for UAV Blades Using Artificial Neural Network

“…The paper tape denotes a substantial modification of the aerodynamics of the blade and this modification characterizes the noise produced by the blade in its rotation. In recent years, algorithms based on machine learning have been used to detect faults in machine functioning and control [50][51][52][53][54][55][56][57][58]. First, acoustic measurements were performed in an anechoic chamber and then these were analyzed to characterize the phenomenon [59][60][61][62][63][64][65].…”

Bearing Fault Diagnosis Based on Natural Adaptive Moment Estimation Algorithm and Improved Octave Convolution

“…Zhou et al [20] proposed a bearing fault diagnosis model based on improved stacked recurrent neural network to solve the problem of gradient disappearance through a gating unit. Zhuang et al [21] proposed a network model consisting of dilated convolution, gate convolution, and residual network. The dilated convolution increases the local receptive field, thereby increasing the receiving domain of the convolution kernel.…”