Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

Yuan, Laohu; Lian, Dongshan; Xue, Kang; Chen, Yuanqiang; Zhai, Kejia

doi:10.1109/access.2020.3012053

Cited by 114 publications

(69 citation statements)

References 39 publications

(43 reference statements)

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“…WT supports flexible resolution change according to frequency value, which is widely used for addressing the shortcomings of STFT. Yuan et al used Continuous Wavelet Transform (CWT) to refine the analysis of signal at multiscale [20]. Yan et al analyzed overall the implementation of various types of WT, which are CWT, Discrete Wavelet Transform (DWT), Second-Generation Wavelet Transform (SGWT), and Wavelet Packet Transform (WPT) [21].…”

Section: A Related Workmentioning

confidence: 99%

“…Moreover, CNN has also received attention owing to its sophisticated image processing ability, which can be adopted in BFD with the 2-D signal representations. Yuan et al combined CNN and SVM to build a network framework for BFD [18]; Chen et al proposed a combination between Cyclic Spectral Coherence and CNN to enhance fault recognition ability [29]. In an effort to boost the generalization ability and avoid overfitting in cases of lacking labeled samples, Te Han et al proposed an adversarial learning framework [30].…”

Section: A Related Workmentioning

confidence: 99%

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Efficient Fault Diagnosis of Rolling Bearings Using Neural Network Architecture Search and Sharing Weights

2021

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Bearing is one of the most vital components of industrial machinery. The failure of bearing causes severe problems in the machinery. Therefore, continuous monitoring for the bearings is essential rather than regular manual checking, with the requirement for accuracy of prediction and efficiency. This paper proposes a novel intelligent bearing fault condition monitoring and diagnosis method focusing on computation efficiency, which is an important aspect of a continuous monitoring and embedded-based diagnosis device. In the proposed method, acoustic emission signals containing bearing health information are converted into 2-D spectrograms by Constant Q-Transform (CQT) before using a convolutional neural network to infer the bearing state. To reduce the latency while maintaining high accuracy, we propose an efficient search space for neural network architecture search, i.e., a channel distribution search, that automatically obtain the best performing network. Moreover, we present a separation between two processes of condition monitoring and fault diagnosis to save overall computing resources, with a policy of sharing weights in the training process and sharing features in the testing process. The experimental results show that the proposed method reduces about 50% inference time compared to previous methods while achieving an accuracy of 99.82% for eight types of single and compound fault diagnosis for variable rotational speeds.INDEX TERMS Acoustic emission, bearing fault condition monitoring, bearing fault diagnosis, convolutional neural network, neural network architecture search.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Efficient Fault Diagnosis of Rolling Bearings Using Neural Network Architecture Search and Sharing Weights

2021

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“…Another paper used the SVM-CNN model for image classification to investigate indiscretion in rolling bearings after turning the vibration signal (1D) into time-frequency images (2D) and pre-training the ResNet18 network for feature extraction [11]. They achieved an overall accuracy of 98.75% after ten trials after converting the signal into images and pre-training the ResNet18 network for feature extraction.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Signal Decomposition Technique for Transfer Learning Based Industrial Fault Diagnosis

Ruhi¹,

Jahan²,

Uddin³

2021

AETiC

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In the fourth industrial revolution, data-driven intelligent fault diagnosis for industrial purposes serves a crucial role. In contemporary times, although deep learning is a popular approach for fault diagnosis, it requires massive amounts of labelled samples for training, which is arduous to come by in the real world. Our contribution to introduce a novel comprehensive intelligent fault detection model using the Case Western Reserve University dataset is divided into two steps. Firstly, a new hybrid signal decomposition methodology is developed comprising Empirical Mode Decomposition and Variational Mode Decomposition to leverage signal information from both processes for effective feature extraction. Secondly, transfer learning with DenseNet121 is employed to alleviate the constraints of deep learning models. Finally, our proposed novel technique surpassed not only previous outcomes but also generated state-of-the-art outcomes represented via the F1 score.

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“…Although bearing vibration signals usually contain sufficient fault information, they are often nonlinear and nonstationary. Therefore, the extraction of signal features is an important step [10].…”

Section: Introductionmentioning

confidence: 99%

“…A deep learning model consists of a multilayer neural network that extracts and learns features from deep layers of input signals. These algorithms perform outstandingly with a massive amount of data [10]. Because of the numerous advantages like end-to-end problemsolving approach, tendency to handle the complex, highdimensional and massive amount of data, higher efficiency, reliability, and universality, DL algorithms are preferred and implemented in almost every sector.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

2021

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Bearings play a vital role in all rotating machinery, and their failure is one of the significant causes of machine breakdown leading to a profound loss of safety and property. Therefore, the failure of rolling element bearings should be detected early while the machine fault is small. This paper presents the model that detects bearing failures using the continuous wavelet transform and classifies them using a switchable normalization-based convolutional neural network (SN-CNN). State-of-the-art accuracy was achieved with the proposed model using the Case Western Reserve University bearing dataset, which serves as the primary dataset for validating various algorithms for bearing failure detection. Batch normalization techniques were also employed and compared to the proposed model. The spectrogram images were also used as input for further comparison.

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Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

Cited by 114 publications

References 39 publications

Efficient Fault Diagnosis of Rolling Bearings Using Neural Network Architecture Search and Sharing Weights

Efficient Fault Diagnosis of Rolling Bearings Using Neural Network Architecture Search and Sharing Weights

A Novel Hybrid Signal Decomposition Technique for Transfer Learning Based Industrial Fault Diagnosis

Bearing Fault Detection Using Scalogram and Switchable Normalization-Based CNN (SN-CNN)

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