scholarly journals Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3937 ◽  
Author(s):  
Tengda Huang ◽  
Sheng Fu ◽  
Haonan Feng ◽  
Jiafeng Kuang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Time Domain ◽  
Attention Mechanism ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

Recently, deep learning technology was successfully applied to mechanical fault diagnosis. The convolutional neural network (CNN), as a prevalent deep learning model, occupies a place in intelligent fault diagnosis, which reduces the need for human feature extraction and prior knowledge, thereby achieving an end-to-end intelligent fault diagnosis model. However, the data for mechanical fault diagnosis in practical application are limited, the CNN model is too deep and too complex, making it prone to overfitting, and a model with too simple a structure and shallow layers cannot fully learn the effective features of the data. Convolutional filters with fixed window sizes are widely used in existing CNN models, which cannot flexibly select variable pivotal features. The model may be interfered with by redundant information in feature maps during training. Therefore, in this paper, a novel shallow multi-scale convolutional neural network with attention is proposed for bearing fault diagnosis. The shallow multi-scale convolutional neural network structure can fully learn the feature information of input data without overfitting. For the first time, a feature attention mechanism is developed for fault diagnosis to adaptively select features for classification more effectively, where the pivotal feature was emphasized, and the redundant feature was weakened through an attention mechanism. The time frequency representations as the input of the model were obtained from the vibration time domain signals, which contain the complete time domain and frequency domain information of the vibration signals. Compared with the current popular diagnostic methods, the results show that the proposed diagnostic method has fairly high accuracy, and its performance is superior to the existing methods. The average recognition accuracy was 99.86%, and the weak recognition rate of I-07 and I-14 labels was improved.

scholarly journals Rolling Bearing Fault Diagnosis Algorithm Based on Overlapping Group Sparse Model-Deep Complex Convolutional Neural Network

2021 ◽  
Author(s):  
FENGPING AN ◽  
Jianrong Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Operating Conditions ◽  
Sparse Model ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale

Abstract As the key component of a mechanical system, rolling bearings will cause paralysis of the entire mechanical system once they fail. In recent years, considering the high generalization ability and nonlinear modeling ability of deep learning, a rolling bearing fault diagnosis method based on deep learning has been formed, and good results have been achieved. However, because this kind of method is still in the initial development stage, its main problems are as follows. First, it is difficult to extract the composite fault signal feature of rolling bearing. Second, the existing deep learning rolling bearing fault diagnosis methods cannot well consider the problem of multi-scale information of rolling bearing signals. Therefore, this paper first proposes the overlapping group sparse model. It constructs weight coefficients by analyzing the salient features of the signal. It uses convex optimization techniques to solve the sparse optimization model, and applies the method to the feature extraction of rolling bearing composite faults. For the problem of multi-scale feature information extraction of rolling bearing composite fault signals, this paper proposes a new deep complex convolutional neural network model. This model fully considers the multi-scale information of rolling bearing signals. The complex information in this model not only contains rich representation ability, but also can extract more scale information. Finally, the classifier of this model is used to identify rolling bearing faults. Based on this, this paper proposes a new rolling bearing fault diagnosis algorithm based on overlapping group sparse model-deep complex convolutional neural network. The experimental results show that the method proposed in this paper can not only effectively identify rolling bearing faults under constant operating conditions, but also accurately identify rolling bearing fault signals under changing operating conditions. Additionally, the classification accuracy of the method proposed in this paper is greatly improved compared with traditional machine learning methods. It also has certain advantages over other deep learning methods.

scholarly journals Bearing Fault Diagnosis via Improved One-Dimensional Multi-Scale Dilated CNN

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7319
Author(s):  
Jiajun He ◽  
Ping Wu ◽  
Yizhi Tong ◽  
Xujie Zhang ◽  
Meizhen Lei ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Vibration Signals ◽  
Maintenance Costs ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Method

Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.

scholarly journals Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1088 ◽  
Author(s):  
Gaowei Xu ◽  
Min Liu ◽  
Zhuofu Jiang ◽  
Dirk Söffker ◽  
Weiming Shen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Ensemble Learning ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Multi Level

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

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