Fault Diagnosis of Bearings with Variable Rotational Speeds Using Convolutional Neural Networks

Tra, Viet; Kim, Jong‐Myon

doi:10.1007/978-981-13-0341-8_7

Cited by 7 publications

(10 citation statements)

References 12 publications

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“…Table 3 shows the accuracy of the proposed FD method, where the results are compared to four existing AE-based FD methods for compound faults detection under variations in the rotational speed. Firstly, we compared two CNN-based FD methods [10,11] using the spectra of AE signals to create two-dimensional (2D) energy distribution maps (EDMs). The created EDMs fed a generic CNN based on Lenet-5 architecture to extract the bearing fault features.…”

Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

“…After extracting features, in [11], a hybrid ensemble MLP-SVM classifier is used to classify the faults from extracted features. On the other hand, in [10], classification is performed by multilayer perceptron classifiers. Additionally, a stochastic diagonal Levenberg-Marquardt algorithm is used while training to enhance the training process.…”

Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

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Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Pham

Kim

2020

Applied Sciences

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Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

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Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

Section: Diagnosis Accuracy For Compound Bearing Faultsmentioning

confidence: 99%

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Pham

Kim

2020

Applied Sciences

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“…Few studies are focusing on this disadvantage. For instance, in order to reduce the number of Multiply-Accumulate (MAC) operations, simple CNN architectures are used [12] [13], or techniques reducing input image size are considered [11]. Currently, in order to maintain the prediction accuracy, the computational cost measured by MACs of previous CNNbased methods is approximately equivalent to the cost consumed by LeNet-5.…”

Section: Introductionmentioning

confidence: 99%

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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“…Some studies have focused on reducing the number of MAC (multiply-accumulate operations) and the number of parameters, thus indirectly reducing the latency of diagnosis inference. For example, simple CNN architectures were designed [19,20]. The scenario of fault type classification works well on limited-resource systems (e.g., embedded systems) by CNNbased methods using a small input image size or model established by a neural architecture search [21,22].…”

Section: Introductionmentioning

confidence: 99%

2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds

Pham

Kim

2021

Machines

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Bearings prevent damage caused by frictional forces between parts supporting the rotation and they keep rotating shafts in their correct position. However, the continuity of work under harsh conditions leads to inevitable bearing failure. Thus, methods for bearing fault diagnosis (FD) that can predict and categorize fault type, as well as the level of degradation, are increasingly necessary for factories. Owing to the advent of deep neural networks, especially convolutional neural networks (CNNs), intelligent FD methods have achieved significantly higher performance in terms of accuracy. However, in addition to accuracy, the efficiency issue still needs to be weathered in complicated diagnosis scenarios to adapt to real industrial environments. Here, we introduce a method based on multi-output classification, which utilizes the correlated features extracted for bearing compound fault type classification and crack-size classification to serve both aims. Additionally, the synergy of a time–frequency signal processing method and the proposed two-dimensional CNN helped the method perform well under the condition of variable rotational speeds. Monitoring signals of acoustic emission also had advantages for incipient FD. The experimental results indicated that utilizing correlated features in multi-output classification improved both the accuracy and efficiency of multi-task diagnosis compared to conventional CNN-based multiclass classification.

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Fault Diagnosis of Bearings with Variable Rotational Speeds Using Convolutional Neural Networks

Cited by 7 publications

References 12 publications

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

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

2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds

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