Real-Time Monitoring of High Voltage Switch Based on CAD

Long, Di; Xiao, Ye; Hu, Feng; Liang, Yong; Tan, Jianrong; You, Yong

doi:10.1155/2022/9136046

Cited by 4 publications

(2 citation statements)

References 14 publications

(11 reference statements)

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“…In this case, the equipment condition monitoring and fault diagnosis technology can be produced and developed [12,13]. It generally requires the real-time monitoring of the status of the equipment during operation to determine the overall and local operation of the equipment [14][15][16][17]. One of the key technologies to ensure the safety and reliability of complex systems is to determine the reasonable maintenance time and formulate the corresponding maintenance system through real-time condition monitoring and fault diagnosis of electromechanical equipment.…”

Section: Introductionmentioning

confidence: 99%

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

Zhao

2022

Mathematical Problems in Engineering

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The operating environment of mechanical equipment is complex, and it is in high-intensity working conditions for a long time. The condition monitoring and fault diagnosis of equipment are very important. As a kind of precision part commonly used in mechanical equipment, the healthy operation of the rolling bearing is a necessary condition to ensure the reliable operation of the whole equipment. This research takes rolling bearing as the research object and is devoted to mechanical fault diagnosis analysis and system design. This paper studies the structure and working principle of rolling bearing, analyzes the types and locations of rolling bearing faults, and puts forward the overall framework of the fault diagnosis system and the workflow of the fault diagnosis system. This paper also studies the relevant theory of deep learning, proposes a neural network model framework for rolling bearing fault diagnosis, and preprocesses the rolling bearing vibration data. It uses a dropout algorithm and GRU to reduce the model parameter size and reduce the risk of overfitting.

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Section: Introductionmentioning

confidence: 99%

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

Zhao

2022

Mathematical Problems in Engineering

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

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confidence: 99%