Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks

Appana, Dileep Kumar; Prosvirin, Alexander E.; Kim, Jong‐Myon

doi:10.1007/s00500-018-3256-0

Cited by 71 publications

(39 citation statements)

References 26 publications

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“…Experiment 1 (the test bearing data set of Western Reserve University). In order to verify the superiority of the proposed method, the XCN model proposed in the previous section is compared with other deep learning algorithms for nearly three years: DWAE+ELM [23] which is based on deep wavelet autoencoder with extreme learning machine, CapsNet which is based on standard capsule neural network, MPE+ISVM+BT [22] which is based on multiscale permutation entropy and improved support vector machine based binary tree, AE+ES+CNN [26] which is based on an acoustic emission analysis-based bearing fault diagnosis invariant under fluctuations of rotational speeds using envelope spectrums and a convolutional neural network, and DBN [25] which is based on the standard deep belief network. Finally, the test accuracy of each algorithm is shown in Table 2 and Figure 15.…”

Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

“…In addition, there are other ways, such as improved ensemble local mean decomposition (IELMD) [16,17], kernel regression residual [18], and modified variable modal decomposition (MVMD) [19,20]. Methods based on big data and machine learning or 2 Complexity deep learning include support vector machine (SVM) [21,22], extreme learning machine (ELM) [23], kernel extreme learning machine (KELM) [24], deep belief network (DBN) [25], and convolutional neural network (CNN) [26,27]. In general, these methods can solve most classification problems well.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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“…In these studies, the MOMEDA has been utilized to extract the fault period impulse component as features, which is the demodulated signal. Other papers developed deep neural network (DNN)-based bearing fault diagnosis methods [9][10][11][12][13][14]. DNN-based bearing diagnosis methods are powerful tools to extract informative features by learning feature representations from a large amount of raw data.…”

Section: Introductionmentioning

confidence: 99%

“…of convolutional neural network (CNN) and DNN-based approaches. From these papers we could conclude that the CNN-based techniques are much better than DNN-based methods in terms of fault diagnosis performance [3,12,15,16]. Although DNN or CNN-based methods have achieved high classification accuracy, there are still two issues that must be resolved to make these methods highly applicable to real applications.…”

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confidence: 99%

Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

Kim

2020

Applied Sciences

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Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure. of convolutional neural network (CNN) and DNN-based approaches. From these papers we could conclude that the CNN-based techniques are much better than DNN-based methods in terms of fault diagnosis performance [3,12,15,16]. Although DNN or CNN-based methods have achieved high classification accuracy, there are still two issues that must be resolved to make these methods highly applicable to real applications. The first issue is that the trained neural network, in general, can be only reliable on the specific machine since the patterns of the raw signals strongly depend on the operating conditions of the machinery such as load, installation, external vibration, etc. The second concern is that the trained feature representation is uninterpretable due to the black box-like operation of the neural networks.This paper proposes a new CNN-based rolling element bearing fault diagnosis approach to resolve the aforementioned problems. To address the first issue, the proposed method utilizes the normalized bearing characteristic components (NBCC) as the input data of CNN rather than raw AE signal itself. Since the bearing characteristic frequencies are induced by appearing bearing failures, NBCC is a more effective representation for diagnosing the bearing failure symptoms. To resolve the second issue, this paper applies the gradient-weighted class activation mapping (Grad-CAM) to visualize important regions in NBCC. According to the literature, Grad-CAM is a promising method that provides visual explanations of the classification result of a CNN in object detection and recognition [17].The remainder of this paper is organized as follows. Section 2 introduces the proposed methodology for diagnosing rolling element bearing faults using AE signals. In Section 3 the bearing fault simulator used for collecting AE signals is presented. The fault diagnosis results demonstrated and discussed in Section 4. Finally, Section 5 contains the concluding remarks. Proposed...

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“…The negative sampling algorithm is processed based on POS tagging and transition probability matrix. Appana et al (2018) suggest a reliable fault diagnosis technique for bearings with varying rotational speeds using a CNN-based method that learns about the bearing faults from the envelope spectrums (ESs) of the raw acoustic emission (AE) signals. The reliable fault diagnosis technique performs based on preprocessing raw AE signal as envelope spectrum and CNN.…”

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confidence: 99%

Advanced computer science and applications for soft computing of converged IT environments

Pan

2018

Soft Comput

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IntroductionThis special issue is to gather new soft computing trends and methodological recent advances on a wide range of problems arising in different fields to handle practical data of converged environment. Many advanced computational methods have been successfully applied to a range of optimization and classification problems in soft computing, but there are still many practical problems tackled by traditional methods that are generally difficult to solve experimentally in practical converged data. More specifically, many computational problems arising in the fields of scientific programming have been addressed in AI, HPC, large-scale data mining, etc., that handles practical converged data. Submissions are welcomed on scientific programming applied to optimization in practical data. We invite researchers and practitioners to submit their original research articles and theoretical articles.This special issue is soliciting high-quality technical papers addressing research practices and challenges in the areas of ubiquitous computing. It will reflect the state of the art of the computational methods, involving theory, algorithm, numerical simulation, error and uncertainty analysis, and/or novel application of new processing techniques in engineering, science, and other disciplines related to ubiquitous computing.

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Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks

Cited by 71 publications

References 26 publications

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

Advanced computer science and applications for soft computing of converged IT environments

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