Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain

Xiao, Yiming; Shao, Haidong; Han, Song-Yu; Huo, Zhiqiang; Wan, Jiafu

doi:10.1109/tmech.2022.3177174

Cited by 193 publications

(73 citation statements)

References 37 publications

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“…Roller bearing condition monitoring using DNN with two branches—one branch accounting for feature engineering (pattern recognition and extraction) and the other branch taking care of the final fault classification [ 15 ]—was performed by Guo et al The convolutional long short-term memory (C-LSTM) approach was employed by Zhao et al to monitor the condition of a tool [ 16 ]. Xiao et al performed a study into the adoption of a novel joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain [ 17 ]. Cai et al examined a data-driven methodology for fault diagnosis in a permanent magnet synchronous motor using Bayesian networks [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Vasan

Sridharan

Sreelatha

et al. 2023

Sensors

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Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer.

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Section: Introductionmentioning

confidence: 99%

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Vasan

Sridharan

Sreelatha

et al. 2023

Sensors

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“…This approach is appropriate for modern electrical machines, especially under nonstationary conditions [10]. Xiao et al firstly took cross-domain case from simulation domain to experimental domain into consideration, and developed promising joint adaptation network, which contributes to unsupervised transfer fault diagnosis [11]. Wang et al exploited a novel convolutional deep belief network, which is applicable to fault diagnosis [12].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

Zhu

Wang

et al. 2023

IEEE Internet Things J.

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Induction motors (IMs) are used extensively as driving actuators in electric vehicles. Motor rotors are prone to defects in the die casting procedure, which can significantly reduce the production quality. Benefitting from the development of Internet of things (IoT) techniques and edge computing, this study designed an instrumentation system for the fast inspection of rotor defects to meet the objectives of efficient and high-quality rotor production. First, an electromagnetic sensing device is designed to acquire the induced voltage signal of the rotor under investigation. Second, a residual multiscale feature fusion convolutional neural network model is designed to extract the hierarchical features of the signal, to facilitate defect recognition. The developed algorithm is deployed into a cost-effective edge computing node that includes a signal acquisition circuit and a Raspberry Pi microcontroller. The conducted experimental studies show that this implementation can achieve an inference time of less than 200 ms and accuracy of more than 99 %. It is shown that the designed system exhibits superior performance when compared with conventional methods. The developed, compact and flexible handheld solution with enhanced deep learning techniques shows outstanding potential for use in real-time rotor defect detection.

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“…The transfer learning (TL) 11 , including domain adaptation 12 and deep transfer learning 13 can leverage present knowledge from the source domain to achieve target domain tasks 14 . And domain adaptation is the critical technology to minimise the distribution discrepancy between the source and target domain by exploring domain-invariant features to address the domain shift problem.…”

Section: Introductionmentioning

confidence: 99%

A novel intelligent bearing fault diagnosis method based on signal process and multi-kernel joint distribution adaptation

Xiong¹,

Shen²,

Tang

2023

Sci Rep

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The present research on intelligent bearing fault diagnosis assumes that the same feature distribution is used to obtain training and testing data. However, the domain shift (distribution discrepancy) issue generally occurs in both datasets because of different operational conditions. The domain adaptation techniques are preferably applied for fault diagnosis to handle the domain shift issue. Moreover, collecting sufficient testing data or labelled data in real industries is a challenging task. Therefore, the multi-kernel joint distribution adaptation (MKJDA) with dynamic distribution alignment is proposed for bearing fault diagnosis. This method dynamically joins both the marginal and conditional distributions and uses the multi-kernel to solve the non-linear problems to extract the most effective and robust representation for cross-domain issues. Moreover, it runs with the unlabelled task domain to perform the diagnosis by iteratively updating the pseudo code. The experimental results (two public datasets and one experimental dataset) demonstrated that the proposed method (MKJDA) exhibited stable and robust accuracy while conducting bearing fault diagnosis. It can effectively address the most crucial issue: intelligent diagnosis methods must re-train the model when the distribution differs between the source domain (the model is learned) and the target domain (the learned model is applied).

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Novel Joint Transfer Network for Unsupervised Bearing Fault Diagnosis From Simulation Domain to Experimental Domain

Cited by 193 publications

References 37 publications

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Real-Time Quality Inspection of Motor Rotor Using Cost-Effective Intelligent Edge System

A novel intelligent bearing fault diagnosis method based on signal process and multi-kernel joint distribution adaptation

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