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

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...

AI Technical Summary

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 past, regular maintenance and regular replacement of parts based on experience, and maintenance methods based on experience to estimate the life of parts are likely to cause waste and misjudgment, and bring potential safety hazards, so they cannot meet the technical requirements. Requirements for personnel intelligent fault diagnosis and online status monitoring

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