A Novel Method of Bearing Fault Diagnosis in Time-Frequency Graphs Using InceptionResnet and Deformable Convolution Networks

Li, Shaobo; Yang, Wanli; Zhang, Ansi; Liu, Huibin; Huang, Jinyuan; Li, Chuanjiang; Hu, Jianjun

doi:10.1109/access.2020.2995198

Cited by 21 publications

(18 citation statements)

References 32 publications

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“…The Siamese CNN was designed by Zhang et al [ 29 ]. PSDAN, FSM3, DeIN and HCAE and were proposed in [ 26 , 52 , 53 , 54 ], respectively. The details of the comparison methods are shown in Table 4 .…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

A Siamese Vision Transformer for Bearings Fault Diagnosis

Bai

et al. 2022

Micromachines

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Fault diagnosis methods based on deep learning have progressed greatly in recent years. However, the limited training data and complex work conditions still restrict the application of these intelligent methods. This paper proposes an intelligent bearing fault diagnosis method, i.e., Siamese Vision Transformer, suiting limited training data and complex work conditions. The Siamese Vision Transformer, combining Siamese network and Vision Transformer, is designed to efficiently extract the feature vectors of input samples in high-level space and complete the classification of the fault. In addition, a new loss function combining the Kullback-Liebler divergence both directions is proposed to improve the performance of the proposed model. Furthermore, a new training strategy termed random mask is designed to enhance input data diversity. A comparative test is conducted on the Case Western Reserve University bearing dataset and Paderborn dataset and our method achieves reasonably high accuracy with limited data and satisfactory generation capability for cross-domain tasks.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

A Siamese Vision Transformer for Bearings Fault Diagnosis

Bai

et al. 2022

Micromachines

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“…To verify the advantages of the proposed model, as shown in Table 2, several popular models are compared, using three types of time series input methods (Siamese-based CNN [35], PSDAN [45], and WDCNN [46]) and three types of time-frequency input methods (SCNN, HCAE [42],s and DeIN [47]). e Siamese-based CNN was designed by [35].…”

Section: Comparison Methods and Evaluation Metricsmentioning

confidence: 99%

“…WDCNN, in which a wide convolution kernel was used in the front of the network, was proposed in [46]. DeIN was proposed in [47]. SCNN is a common CNN that follows a softmax at the end of the same structure with the encoder of HMN.…”

Section: Comparison Methods and Evaluation Metricsmentioning

confidence: 99%

A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data

et al. 2022

Computational Intelligence and Neuroscience

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Intelligent fault diagnosis methods based on deep learning have achieved much progress in recent years. However, there are two major factors causing serious degradation of the performance of these algorithms in real industrial applications, i.e., limited labeled training data and complex working conditions. To solve these problems, this study proposed a domain generalization-based hybrid matching network utilizing a matching network to diagnose the faults using features encoded by an autoencoder. The main idea was to regularize the feature extractor of the network with an autoencoder in order to reduce the risk of overfitting with limited training samples. In addition, a training strategy using dropout with random changing rates on inputs was implemented to enhance the model’s generalization on unseen domains. The proposed method was validated on two different datasets containing artificial and real faults. The results showed that considerable performance was achieved by the proposed method under cross-domain tasks with limited training samples.

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“…Moreover, because it has a deeper layer's presentation, the ResNet makes it possible to design deeper learning applications that deal with more complicated real-world problems. Furthermore, it has been shown in the literature that this type of deep network facilitates faster convergence than that achieved by a CNN which does not have a skip connection function [27][28][29].…”

Section: The Residual Neural Network (Resnet) For Feature Learningmentioning

confidence: 99%

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

Najeh

Lundberg

Kerrouche

2021

Sensors

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The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.

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A Novel Method of Bearing Fault Diagnosis in Time-Frequency Graphs Using InceptionResnet and Deformable Convolution Networks

Cited by 21 publications

References 32 publications

A Siamese Vision Transformer for Bearings Fault Diagnosis

A Siamese Vision Transformer for Bearings Fault Diagnosis

A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data

Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings

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