State monitoring and fault diagnosing method for rotating mechanical equipment

Abstract

The invention discloses a state monitoring and fault diagnosing method for rotating mechanical equipment based on systematic cluster analysis and Fisher discriminant analysis. The method includes the steps that empirical mode decomposition is carried out on obtained monitoring data in a normal state and various fault states to extract energy feature vectors; based on systematic cluster analysis, the separability among samples is verified, and the influence on the dispersibility of sample data is eliminated; on the basis, discriminant analysis functions are extracted, a discrimination totality is built, and faults are diagnosed according to the real-time state monitoring data of the rotating mechanical equipment. Hot problems of online fault diagnosis for rotating mechanical equipment are solved, and intelligent fault diagnosis for the rotating mechanical equipment is achieved. A state monitoring and fault diagnosing model can be set up without the fault state data of a plenty of samples or full-life state monitoring data, dependency on the data is reduced, operability is improved, and the state monitoring and fault diagnosing method has high engineering applicability.

Description

technical field [0001] The invention belongs to the technical field of intelligent fault diagnosis of rotating mechanical equipment, and in particular relates to a fault diagnosis method for rotating mechanical equipment based on the combination of system cluster analysis (HCA) and Fisher discriminant analysis (FDA). Background technique [0002] At present, the unexpected downtime of equipment failure is still a major problem existing in missile weapon systems, especially ground weaponry, which will cause serious impact on mission and safety. For the ground equipment related to the missile weapon system, equipment maintenance and repair, as an effective way, can ensure the continuous high reliability of the weapon equipment to a certain extent. However, passive after-the-fact maintenance is difficult to avoid the occurrence of failures, which will result in excessive downtime, delay or even failure of missile launch missions, and high maintenance costs and continuation loss...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide a method for state monitoring and fault diagnosis of rotating machinery equipment based on the combination of system cluster analysis and Fisher discriminant analysis, so as to solve the problem of state monitoring and fault diagnosis of rotating machinery equipment related to ground equipment of missile weapon systems. In terms of fault diagnosis, existing methods rely on full-life degradation data, and the accuracy of fault diagnosis is low and the robustness is poor.

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