Method for monitoring and diagnosing equipment operation states according to vibration algorithm

Abstract

The invention discloses a method for monitoring and diagnosing equipment operation states according to a vibration algorithm. By the method, the equipment operation states can be monitored, time domains and frequency domains of vibration signals of equipment and operation trends of the equipment can be analyzed, residual service life of the equipment can be predicated according to an intelligent learning algorithm, proper signal collection strategies can be selected according to actual conditions to collect the vibration signals, change conditions of the vibration signals can be monitored, some related technical parameters can be monitored and warned, time domain waveforms of the vibration signals can be monitored, the equipment operation states can be diagnosed according to related features of the signals through signal analysis, and fault positions and fault types of the equipment can be determined. The method plays a large role in reducing maintenance cost and production cost and improving economical and social benefits, and has significance in avoiding huge economic losses and catastrophic accidents.

Description

technical field [0001] The invention relates to the field of equipment fault diagnosis, in particular to a method for monitoring and diagnosing the operating state of equipment by using a vibration algorithm. Background technique [0002] The production equipment in modern enterprises is increasingly large-scale, continuous, high-speed and automated, which has also become the main feature of modern large-scale enterprise production. The structure and composition of the equipment are very complex, the production scale is very large, and the connection between various parts is also particularly close. Although this helps to increase productivity, reduce product production costs and improve product quality, on the other hand, the loss caused by downtime due to mechanical equipment failure has greatly increased. Modern large-scale production industries, such as petroleum, petrochemical, chemical, electric power, and steel, all adopt single-machine, full-load, and continuous pro...

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Problems solved by technology

[0013] However, these existing equipment fault diagnosis methods generally do not use relevant signal preprocessing methods when collecting signals, and directly analyze the collected signals, and the interference such as noise contained therein seriously affects the accuracy of signal feature extraction; The accuracy of the analysis method is limited. Since the current signal characteristic analysis methods are mainly time-domain analysis methods and signal frequency-domain analysis methods based on Fourier transform, the frequency-domain analysis method based on Fourier transform is only suitable for stationary signals. , and the field vibration signal is a non-stationary signal with a lot of noise, which makes it difficult to improve the accuracy of signal feature extraction; the existing technology can only monitor the working conditions of the equipment and realize a simple alarm function, but cannot Analyze the operation trend of the equipment, and it is not possible to analyze the operation trend of the equipment with high precision through the calculation of relevant parameters

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