Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image

Sun, Yongjian; Li, Shaohui; Wang, Yaling; Wang, Xiaohong

doi:10.1016/j.ymssp.2021.107817

Cited by 95 publications

(37 citation statements)

References 21 publications

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“…Recently, deep learning has achieved considerable progress in computer vision [4,5], speech and natural language processing [6], product defect detection [7], and road planning [8]. Expectedly, an increasing number of researchers have applied deep learning techniques to fault diagnosis and proposed intelligent fault diagnosis methods [9][10][11][12][13][14][15][16]. Hasan et al [17] proposed an explainable AI-based model for bearings fault diagnosis.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…Distance transform [24,25] is an operation for binary image, which can be used in the template matching method to estimate target pose [26]. Different distance measures will produce different transformation results, among which the most commonly used distances are Euclidean distance [27], Manhattan distance [28], and chamfer distance [29]. Euclidean distance represents the exact distance between the template and the object image, and the other distances are approximate expressions of Euclidean distance [30].…”

Section: Introductionmentioning

confidence: 99%

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

Peng

Zhu

Zhang

et al. 2022

Sensors

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The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

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“…[3], [4]. Time-frequency features are usually abstracted by means of short time Fourier transform [5], empirical mode decomposition [6], Wigner-Ville distribution [7], wavelet analysis [8], HHT time-frequency analysis, and high order spectral analysis [9]. However, the efficiency of the extraction process is of great importance for real-time diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis using a novel coding-statistic feature combined with NNC

Qiu¹,

Zhao²

2022

J. vibroeng.

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The failures of rolling bearings usually cause the breakdown of rotating machinery. Therefore, bearing fault diagnosis is receiving more and more attentions. In this paper, a new coding-statistic feature is proposed for bearing fault diagnosis. Firstly, a waveform coding matrix (WCM) is drawn from each signal using a coding algorithm then a statistical feature is extracted from the WCM with a pre-defined dictionary. Secondly, all statistical features are processed using two-dimensional principal component analysis (2DPCA) to reduce redundant information and dimensionality. Finally, a nearest neighbor classifier (NNC) is employed to classify the bearing faults. Two bearing fault classification problems are utilized to demonstrate the effectiveness of the proposed scheme. Experimental results show that an excellent performance could be accomplished with the proposed scheme.

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Fault diagnosis of rolling bearing based on empirical mode decomposition and improved manhattan distance in symmetrized dot pattern image

Cited by 95 publications

References 21 publications

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

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

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

Bearing fault diagnosis using a novel coding-statistic feature combined with NNC

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