Bearing Fault Automatic Classification Based on Deep Learning

Yang, Yanli; Fu, Peiying; He, Yichuan

doi:10.1109/access.2018.2880990

Cited by 56 publications

(33 citation statements)

References 26 publications

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“…Similarly, [143] talks about an automatic classification based on deep learning in which faulty signals are clustered without human knowledge. Here, a dataset, in which each sample is given a random label, is configured after extracting the features of vibration signals from the frequency-domain and used to train DNN to obtain an initial classification.…”

Section: Deep Transfer Learning and Domain Adaptation Methodsmentioning

confidence: 99%

“…The variance is high because the bands of measurements in the same file are dissimilar. Additionally, not all the frequency components are regular; some are occasionally large or small [143]. So, using preprocessors like FFT or other signal processing techniques for feature extraction can be tough.…”

Section: Weaknesses Of Cwru Bearing Datasetmentioning

confidence: 99%

“…The methodology implemented in this review paper is like this: the research works which implement deep learningbased approaches using the CWRU dataset, more specifically Drive-End (DE) defects data and normal-baseline data, for bearing fault detection and diagnosis are overviewed here. Except for [143], all the papers summarized here use DE (12k or 48k) faulty data and normal-baseline data as healthy bearing data in their deep learning models, whereas [143] uses all the records (drive-end, normal-baseline and fan-end data) of the CWRU bearing dataset.…”

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset.

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Section: Deep Transfer Learning and Domain Adaptation Methodsmentioning

confidence: 99%

Section: Weaknesses Of Cwru Bearing Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Dazhong Ma . the Fourier transform [5] is strongly adaptive to stationary signals, it does not reflect its characteristics for nonstationary signals. While wavelet transform can be applied to deal with nonstationary signals, the choice of wavelet base is quite complicated [6].…”

Section: Introductionmentioning

confidence: 99%

The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

2020

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The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

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“…On[22], the Kmeans algorithm is combined with a GAN network and an autoencoder to create a dimensional reduction of the dataset to detect failures reaching a peak accuracy of 94.69%. In[23], a Deep Neural Network is employed for the fault detection, beginning with a feature extraction from the frequency spectrum of the signals and the use of Principal Component Analysis (PCA) to reduce the data dimension. After that, the network is trained based on the 3D PCA map of each signal.…”

mentioning

confidence: 99%

FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing Failure Detection Method

Viola

Chen

Wang

2019

2019 1st International Conference on Industrial Artificial Intelligence (IAI)

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Failure detection is employed in the industry to improve system performance and reduce costs due to unexpected malfunction events. So, a good dataset of the system is desirable for designing an automated failure detection system. However, industrial process datasets are unbalanced and contain little information about failure behavior due to the uniqueness of these events and the high cost for running the system just to get information about the undesired behaviors.

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Bearing Fault Automatic Classification Based on Deep Learning

Cited by 56 publications

References 26 publications

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

The Feature Extraction and Diagnosis of Rolling Bearing Based on CEEMD and LDWPSO-PNN

FaultFace: Deep Convolutional Generative Adversarial Network (DCGAN) based Ball-Bearing Failure Detection Method

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