Bearing condition monitoring and management method and system for cloud, fog and edge coordination

Abstract

The invention discloses a bearing status monitoring and management system and method based on cloud, fog, and edge end collaboration, which acquires bearing operating signals, performs signal processing such as noise reduction and feature extraction, and uploads the results to the fog end device, and the fog end device according to The information uploaded from the edge side updates the bearing digital twin model and performs fault diagnosis and life prediction of digital-analog fusion, uploads the results and some edge side information to the cloud, and accepts the customized bearing fault diagnosis and life prediction model released by the cloud; the cloud accepts Fog end information, indexing spatiotemporal data and customizing bearing fault diagnosis and life prediction models for fog end calculations, and providing predictive maintenance solutions. This solution overcomes the shortcomings of the existing bearing operation and maintenance system based on big data technology, such as less computing levels, single function, and data-driven, and provides an efficient computing architecture and accurate diagnosis and prediction solutions for predictive maintenance.

Description

technical field [0001] The invention belongs to the technical field of mechanical diagnosis intelligence and digitization, and in particular relates to a bearing state monitoring and management method and system coordinated by cloud, fog and edge terminals. Background technique [0002] High-end bearings are the key load-bearing and transmission components of rotating machinery in major equipment such as wind turbines and high-speed rail, and their service life determines the refurbishment period and reliability of the complete machine. The working conditions of high-end bearings are complex, with extreme working conditions such as high speed, high temperature, overload shock, and large-scale fluctuations in working conditions. This makes the bearing prone to peeling, pitting and slipping during long-term use, and eventually leads to bearing failure. The online bearing condition monitoring and management system can dynamically obtain the bearing operating status in real time...

AI Technical Summary

Problems solved by technology

[0004] Through literature research, it can be found that most of the existing bearing condition monitoring and management systems are only implemented on a single platform at the edge or cloud. Due to the huge pressure of data transmission, it is difficult for the equipment to achieve real-time feedback to the monitored equipment, and the addition of fog end equipment can not only share the burden of calculation and transmission for the former two, but also provide a good personalized diagnosis platform

In addition, most of the existing cloud (fog) edge collaborative bearing condition monitoring and management systems can only realize the data-driven bearing fault diagnosis function, and cannot accurately realize the predictive maintenance function, while the digital twin bearing life prediction combined with digital and analog can accurately simulate Damage expansion of faulty bearings, calculation of remaining life, and providing a strong basis for spare parts allocation

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