A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions

Tang, Tang; Hu, Tianhao; Chen, Ming; Lin, Ronglai; Chen, Guorui

doi:10.1177/0954406220902181

Cited by 24 publications

(10 citation statements)

References 24 publications

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“…have been successfully used in order to create additional faulty data in the training. Tang et al 2020 102 developed a deep CNN approach based on knowledge fusion for analysing bearing defects in a variety of operational scenarios. Via the use of multi-sensors and narrow-band decomposition methods, an information fusion technique was implemented in this learning to increase the characteristic demonstration capability with the transferability of analysis systems.…”

Section: Based Rotating Machines Fault Diagnosismentioning

confidence: 99%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

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Rotating machine faults are unavoidable; thus, early diagnosis is essential to avoid further damage to the machine or other machine attached to it. Various signal analysis based conventional techniques have been developed and used in the industries to identify various defects in the rotating machines. In last two decades, researchers have shifted their focus to automated or intelligent fault diagnosis based on Artificial Intelligence (AI) techniques due to a variety of issues in conventional fault analysis techniques, such as a dependence on machine operating circumstances, human interference, and expert abilities. In AI based techniques, various machine learning (ML) and deep learning (DL) techniques have been successfully applied for fault diagnosis of various rotating machines. From last half decade DL have been gaining popularity due to its attractive characteristic of automated feature learning and solving big data, unbalanced data, big computational burden and over-fitting problems of conventional ML techniques. Advances in DL methodologies have prompted interest in DL based intelligent fault diagnosis in the industry in the last five to 6 years. This review paper summarizes recent research and developments on DL based fault diagnosis in the last five to 6 years for various critical rotating machineries in industry such as electric motors, rotor-bearing systems, gear and gearbox, wind turbines, pumps, and compressors.

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Section: Based Rotating Machines Fault Diagnosismentioning

confidence: 99%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

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“…The results show that the method can be efficiently applied to diagnosis cases under new working conditions. Tang et al 20 investigated the robustness of deep CNN. The transferability and generalization performance of the method is demonstrated on different datasets.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of planetary gear backlash based on motor current and Fisher criterion optimized sparse autoencoder

Zhang

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Planetary gear reducer is widely applied in various transmission equipment, and its performance highly affects the operation of a machine. The appearance of unreasonable backlash in planetary gear reducer may lead to undesirable vibration, which may accelerate the degradation of equipment and eventually cause premature failure. In traditional condition-based monitoring (CBM), sensors such as accelerometers have been utilized to detect the fault of planetary gear. However, the complexity and integration of planetary gear limit the installation and application of vibration sensors in practice. In this case, the current signal from motor, as a convenient real-time monitoring approach, is introduced into the CBM of the planetary gear. In this paper, a fault diagnosis method based on drive motor current signal analysis (MCSA) is presented to identify the backlash fault in a planetary gear reducer. In this method, frequency domain data of the original current signal is found and used to automatically extract fault features. A deep sparse autoencoder (DSAE) extracts the required features. In particular, the Fisher criterion is introduced to evaluate the sensitivity of these features. It then selects the most effective ones for improving diagnostic accuracy as well as diagnostic efficiency. Experimental test data shows that under different load conditions, this method outperforms other typical fault diagnosis methods and exhibits the best performance.

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“…Many real-world problems have high complexity and unknown underlying models which makes them excellent candidates for the application of ML. ML can be applied to various areas of computing to design and programming explicit algorithms with high-performance output, such as in the manufacturing industry, robotics [6], e-commerce, medical applications [7], scientific visualization [8] and fault diagnosis [9].…”

Section: Introductionmentioning

confidence: 99%

Cloud Based Decision Making for Multi-agent Production Systems

Rehman

Pulikottil

Estrada-Jimenez

et al. 2021

Progress in Artificial Intelligence

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The use of multi-agent systems (MAS) as a distributed control method for shop-floor manufacturing control applications has been extensively researched. MAS provides new implementation solutions for smart manufacturing requirements such as the high dynamism and flexibility required in modern manufacturing applications. MAS in smart manufacturing is becoming increasingly important to achieve increased automation of machines and other components. Emerging technologies like artificial intelligence, cloud-based infrastructures, and cloud computing can also provide systems with intelligent, autonomous, and more scalable solutions. In the current work, a decision-making framework is proposed based on the combination of MAS cloud computing, agent technology, and machine learning. The framework is demonstrated in a quality control use case with vision inspection and agent-based control. The experiment utilizes a cloud-based machine learning pipeline for part classification and agent technology for routing. The results show the applicability of the framework in real-world scenarios bridging cloud service-oriented architecture with agent technology for production systems.

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A deep convolutional neural network approach with information fusion for bearing fault diagnosis under different working conditions

Cited by 24 publications

References 24 publications

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Fault diagnosis of planetary gear backlash based on motor current and Fisher criterion optimized sparse autoencoder

Cloud Based Decision Making for Multi-agent Production Systems

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