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

Pham, Minh Tuan; Kim, Jong‐Myon; Kim, Cheol Hong

doi:10.3390/app10207068

Cited by 30 publications

(22 citation statements)

References 18 publications

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“…To verify the efficiency and accuracy of the proposed models, we use the AE signal dataset used in the literature [8], [11], [48]- [50]. We initially evaluate the proposed process's efficiency by metrics of the number of MAC, the number of parameters, and latency in various cases.…”

Section: Methodsmentioning

confidence: 99%

“…Wang et al proposed a transfer learning from AlexNet (2-D CNN) with input images generated from eight different time-frequency analysis methods [11]. Similarly, Tuan et al showed the effectiveness of timefrequency representation; they utilized VGGNet and EfficientNet with a 2-D spectrogram to achieve high accuracy in complicated bearing faults classification [8] [9]. However, a downside of CNN-based methods in the BFD field is the high overhead of computational resources compared with common traditional methods [11].…”

Section: Introductionmentioning

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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…As an example, on the one hand, ref. [155] proposes a CNN-based classification method for diagnosing bearing faults under variable shaft speeds using acoustic signals. These signals are represented by spectrograms to obtain as much information as possible in the time-frequency domain.…”

Section: Mechanical Subsystemmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

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“…In general, the diagnosis issues are solved by modeling the physical model bearing or finding the relationship between bearing defects and the corresponding characteristic of monitoring signals that carry the bearing health information. Various modalities have been utilized for monitoring bearings-such as vibration [2][3][4][5], stator current [6,7], thermal imaging [8], electromagnetic signals [9], and acoustic emission (AE) [10][11][12][13]. In general, these methods are categorized into knowledge-based, physical model-based, and data-driven approaches, with the help of signal processing analysis in the time-, frequency-, or time-frequency domains.…”

Section: Introductionmentioning

confidence: 99%

“…M.T. Pham et al [10] proposed a method based on the efficient-net (CNN) to predict not only bearing fault types but also the level of degradation, but the proposed multiclass classification shapes a network with many classes. This method requires a large amount of computational resources.…”

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

Cited by 30 publications

References 18 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

Data-Driven Fault Diagnosis for Electric Drives: A Review

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

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