Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Hoang, Duy-Tang; Kang, Hyejin

doi:10.1016/j.cogsys.2018.03.002

Cited by 367 publications

(205 citation statements)

References 36 publications

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“…Wen et al [107] used images of vibration signals as the input of a CNN, and achieved significant improvements in their proposed fault diagnosis method. Hoang et al [108] adopted a deep CNN structure in the fault diagnosis of rolling bearings and achieved an extremely high accuracy and robustness under noisy environments. The second is using 2D time-frequency images as the input of the CNN for a diagnosis.…”

Section: Research Status Of Cnn-based Fault Diagnosismentioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge. To provide accurate diagnosis results automatically, numerous studies have been carried out. Among them, signal-based methods are commonly used, which employ signal processing techniques based on the state signal used for extracting features, and further input the features into the classifier for fault recognition. However, their main deficiencies include the following: (1) The features are manually designed and thus may have a lack of objectivity. (2) For signal processing, feature extraction and pattern recognition are conducted using independent models, which cannot be jointly optimized globally. (3) The machine learning algorithms adopted by these methods have a shallow architecture, which limits their capacity to deeply mine the essential features of a fault. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome such deficiencies. Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional data. In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized. The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed, and research ideas for applying DNNs for hydraulic fault diagnosis are proposed. Meanwhile, the research advantages and development trend of DNNs (both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed. Furthermore, the fault characteristics of a complex hydraulic system are summarized and discussed, and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.

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Section: Research Status Of Cnn-based Fault Diagnosismentioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

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“…Rolling bearings are the key components of various rotating machinery that are widely used in all walks of life [1,2]. The failure of the bearings could lead to serious consequences such as grave safety accidents, long breaks of production and great pecuniary losses.…”

Section: Introductionmentioning

confidence: 99%

“…With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. Accordingly, deep learning algorithms have been applied widely in machine health monitoring recently [2]. Duy-Tang Hoang [2] proposed a method for diagnosing bearing faults based on a deep structure of convolutional neural network, and this method has high accuracy and robustness in noisy environment.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, deep learning algorithms have been applied widely in machine health monitoring recently [2]. Duy-Tang Hoang [2] proposed a method for diagnosing bearing faults based on a deep structure of convolutional neural network, and this method has high accuracy and robustness in noisy environment. With deep convolutional neural network as the main structure, Xiang Li [13] proposed a novel domain adaptation method for rolling bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…However, there still exist two shortcomings in traditional REB fault diagnosis methods: (1) A large amount of redundant information is difficult to identify and delete [27]. (2) Aliasing patterns decrease the classification accuracy [27] and increase the complexity of fault diagnosis. Although deep learning techniques could extract more representative features from bearing fault data adaptively, they usually have high computational cost, slow convergence speed and unavoidable randomness [25].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets

et al. 2019

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Rolling element bearings (REB) are widely used in all walks of life, and they play an important role in the health operation of all kinds of rotating machinery. Therefore, the fault diagnosis of REB has attracted substantial attention. Fault diagnosis methods based on time-frequency signal analysis and intelligent classification are widely used for REB because of their effectiveness. However, there still exist two shortcomings in these fault diagnosis methods: (1) A large amount of redundant information is difficult to identify and delete. (2) Aliasing patterns decrease the methods’ classification accuracy. To overcome these problems, this paper puts forward an improved fault diagnosis method based on tree heuristic feature selection (THFS) and the dependent feature vector combined with rough sets (RS-DFV). In the RS-DFV method, the feature set was optimized through the dependent feature vector (DFV). Furthermore, the DFV revealed the essential difference among different REB faults and improved the accuracy of fault description. Moreover, the rough set was utilized to reasonably describe the aliasing patterns and overcome the problem of abnormal termination in DFV extraction. In addition, a tree heuristic feature selection method (THFS) was devised to delete the redundant information and construct the structure of RS-DFV. Finally, a simulation, four other feature vectors, three other feature selection methods and four other fault diagnosis methods were utilized for the REB fault diagnosis to demonstrate the effectiveness of the RS-DFV method. RS-DFV obtained an effective subset of five features from 100 features, and acquired a very good diagnostic accuracy (100%, 100%, 99.51%, 100%, 99.47%, 100%), which is much higher than all comparative tests. The results indicate that the RS-DFV method could select an appropriate feature set, deeply dig the effectiveness of the features and more exactly describe the aliasing patterns. Consequently, this method performs better in REB fault diagnosis than the original intelligent methods.

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Magnetic tile fault detection of high‐voltage electric machines: A multiview feature selection method via consistent soft label

Chen,

Yang,

Liu

et al. 2023

Circuit Theory & Apps

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Image‐based scheme has attracted wide attention in the fault detection of high‐voltage permanent magnet motors, but it often suffers from the shooting conditions. Multiview feature selection allows multisource information to be fused, which can improve the accuracy and robustness of image detection. Therefore, we propose multiview unsupervised consistency via soft‐label feature selection (MUCSFS). This method constructs consistent pseudo labels through soft labels from clustering affinity of each view sample and builds the model by integrating selection constraints into the graph model. This model filters the fault data set to obtain the feature subset, which is used for clustering. We verify the method's effectiveness by simulating multiview data. The fault clustering experiment on the high‐voltage motors' magnetic tile fault data set confirm that our method can effectively cluster the fault categories.

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Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Cited by 367 publications

References 36 publications

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Rolling Bearings Fault Diagnosis Based on Tree Heuristic Feature Selection and the Dependent Feature Vector Combined with Rough Sets

Magnetic tile fault detection of high‐voltage electric machines: A multiview feature selection method via consistent soft label

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