Rotating machinery fault diagnosis and state monitoring system and method based on deep learning

A condition monitoring system, deep learning technology, applied in neural learning methods, computer parts, pattern recognition in signals, etc. question

Active Publication Date: 2018-10-19
WUHAN UNIV OF TECH
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  • Application Information

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Problems solved by technology

[0003] Before the present invention, there were relatively few products or methods for fault diagnosis and condition monitoring of rotating machinery on the market, and the traditional methods of "after-event maintenance", "planned maintenance" and "scheduled maintenance" were still used more. This method is often very inefficient and not intelligent, and in the

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  • Rotating machinery fault diagnosis and state monitoring system and method based on deep learning

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Embodiment Construction

[0049] In order to better understand the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0050] Such as figure 1 As shown, it includes a housing 1, a speaker 2, a display 6, a memory 10, a CPU 11 and a data acquisition device 18. The housing 1 is provided with a cavity, and the inside of the cavity is set to include an integrated deep learning device, a historical signal database 23, The fault category expert system library 19 and the data acquisition device 18, the integrated deep learning device includes a deep learning module 24, an adaptive integrated strategy module 20, a signal transceiver 5 is arranged at the middle position of the upper end of the housing 1, and the signal transceiver The right side of the transmitter 5 is provided with a loudspeaker 2, the left side of the signal transceiver 5 is provided with a power off button 7, the left side of the power off button 7 is provi...

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Abstract

The invention provides a rotating machinery fault diagnosis and state monitoring system and method based on deep learning. The system includes a shell, a loudspeaker, a display, a storage, central processing unit (CPU) and a data acquisition device, an integrated deep learning device, a historical signal database, a fault category expert system library and the data acquisition device are arrangedinside the shell, a middle position of an upper end part of the shell is provided with a signal transceiver, the right side is provided with the loudspeaker, the display is arranged under the signal transceiver, a USB interface is arranged under the display at the left side, the storage is arranged under the USB interface, the CPU is arranged under the storage, a graphics processing unit (GPU) isarranged under the CPU, a data interface is arranged under the GPU, and all components in the shell are connected together through leads to form an access. The rotating machinery fault diagnosis and state monitoring system based on deep learning is more accurate and convenient in fault diagnosis and state online monitoring of rotating machinery.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis and monitoring of rotating machinery, and in particular relates to a system and method for fault diagnosis and state monitoring of rotating machinery based on deep learning. Background technique [0002] With the rapid development of science and technology, rotating machinery equipment is increasingly developing towards high speed, precision, automation and integration. Rotating machinery mainly includes power devices, such as diesel engines, steam turbines, engines, electric motors, etc. Such as bearings, bearing bushes, spindles, etc. With the diversification of the working environment of rotating machinery, especially when it operates continuously for a long time in a complex and changeable working environment, it is often prone to various failures due to its heavy workload, variable load, and the influence of salt-alkali corrosion and high temperature. . If the fault cannot be diagno...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/00G06N3/08G06F17/30
CPCG06N3/084G06F2218/02G06F2218/08G06F18/23
Inventor 陈辉宫文峰张泽辉管聪高海波
Owner WUHAN UNIV OF TECH
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