Electromechanical equipment fault diagnosis method based on deep neural network

A technology of deep neural network and electromechanical equipment, which is applied in the state monitoring of electromechanical equipment, and solves the field of state monitoring and fault diagnosis of electromechanical equipment in the nuclear power field. Big data processing needs and other issues to achieve the effect of improving investment efficiency and reducing workload

Active Publication Date: 2020-09-15
SHANGHAI NUCLEAR ENG RES & DESIGN INST CO LTD
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Problems solved by technology

However, the complex structure and mechanism of mechanical equipment, coupled with the interference of its harsh operating environment, and the changes in working conditions brought about by its complex tasks, make the analysis, processing and diagnosis of mechanical equipment big data difficult.
The previous intelligent diagnosis algorithm is difficult to meet the needs of big data processing

Method used

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  • Electromechanical equipment fault diagnosis method based on deep neural network
  • Electromechanical equipment fault diagnosis method based on deep neural network
  • Electromechanical equipment fault diagnosis method based on deep neural network

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Embodiment Construction

[0032] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0033] This method proposes a fault diagnosis method for electromechanical equipment based on deep learning theory. This method uses a deep learning network to train the massive data obtained by the electromechanical monitoring system, and obtains the characteristic information contained in the data through multi-layer nonlinear transformation. Complete the fitting of electromechanical state data to fault categories, and realize automatic diagnosis of electromechanical equipment faults.

[0034] Such as figure 1 As shown, the method mainly includes the following steps:

[0035] ●Data collection

[0036] ●Data preprocessing

[0037] ●Deep neural network training

[0038] ●On-line identification of mechanical and electrical equi...

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Abstract

The invention discloses an electromechanical equipment fault diagnosis method based on a deep neural network. The electromechanical equipment fault diagnosis method comprises the steps of data acquisition, data preprocessing, deep neural network training, electromechanical equipment fault online identification and unknown fault automatic learning. The electromechanical equipment fault diagnosis method does not depend on manual selection of fault features, and can learn information contained in the equipment state monitoring data comprehensively. The electromechanical equipment fault diagnosismethod can realize automatic fitting from the equipment state data to the fault category, can reduce the workload of fault diagnosis algorithm development, can realize continuous expansion of the fault diagnosis function through learning of unknown faults, and can improve the investment benefit of the system.

Description

technical field [0001] The invention belongs to the field of state monitoring of electromechanical equipment, and is particularly used for solving the field of state monitoring and fault diagnosis of electromechanical equipment in nuclear power and other fields. Background technique [0002] The health monitoring of electromechanical equipment has entered the era of big data, and big data of electromechanical equipment has become an important resource to reveal the evolution process and mechanism of mechanical failures. However, the complex structure and mechanism of mechanical equipment, coupled with the interference of its harsh operating environment, and the changes in working conditions brought about by its complex tasks, make it difficult to analyze, process and diagnose big data of mechanical equipment. The previous intelligent diagnosis algorithm is difficult to cope with the demand of big data processing. Therefore, artificial intelligence technology represented by ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q10/00
CPCG06N3/08G06Q10/20G06N3/045G06F18/24G06F18/214
Inventor 张健鹏张东生毕道伟匡红波钟华卜江涛刘欢张艳婷
Owner SHANGHAI NUCLEAR ENG RES & DESIGN INST CO LTD
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