Rotating Machinery Fault Diagnosis Method Based on Cloud Genetic Algorithm Optimization Support Vector Machine

Abstract

The invention discloses a rotating machinery fault diagnosis method based on cloud genetic algorithm optimization support vector machine. Firstly, in each fault state of the rotating machinery, working signals are extracted from different sensors, and time-frequency feature extraction and reduction are respectively performed to obtain each The eigenvectors of the working signals are obtained based on these eigenvectors to obtain training samples for each fault state, which are divided into training sample set A and training sample set B. First, the training sample set A is used as the training sample, and cloud genetic algorithm is used to The kernel function and penalty factor of the multi-classification model of the network are pre-optimized for parameters, and then the training sample set B is used to optimize again to obtain a multi-classification model. When the rotating machinery fails, the feature vector is extracted from each sensor and input into the multi-classification model. The model gets a diagnosis. The invention can effectively improve the accuracy and efficiency of fault diagnosis of rotating machinery.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of construction machinery systems, and more specifically relates to a method for fault diagnosis of rotating machinery based on cloud genetic algorithm optimization support vector machine. Background technique [0002] With the modernization of industry and the rapid development of science and technology, rotating machinery equipment, as one of the most widely used equipment in the industry, is increasingly used in the fields of electric power, petrochemical industry, aviation and various military industries. Rotating machinery equipment is constantly developing towards high speed, systematization and automation. The scale of its production system is gradually increasing, and the mechanical structure is becoming more and more complex. Each kind of equipment is interrelated and closely coupled with each other. Higher and higher. In the operation of rotating mechanical equipment, accompanie...

AI Technical Summary

Problems solved by technology

Among them, model-based fault diagnosis requires evaluation of design parameters and working conditions to accurately model the system. For complex rotating mechanical equipment, this diagnosis method is uneconomical; fault diagnosis based on signal processing requires analysis and processing of system acquisition signals. Because model-based fault diagnosis is more economical and has certain reliability, but in some specific environments, there are some uncertain information that will have a certain impact on fault information, so the problem of feature extraction and fault judgment need to be further solved; based on Knowledge of fault diagnosis since historical fault knowledge is not fully able to have high reliability like human experts

At the same time, in today's era of big data, in the actual application of engineering, the existing intelligent fault diagnosis technology has problems such as speed to be improved, accuracy rate is low, and high-precision classification cannot be achieved, which needs to be studied and solved.

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