Fault diagnosis method under action of central frequency convergence trend

Abstract

The invention discloses a fault diagnosis method under the action of a central frequency convergence trend. The method comprises the following steps: (1) collecting a dynamic signal x (t) of rotary mechanical equipment; (2) setting initial decomposition parameters of the variational model; (3) decomposing the dynamic signal x (t) by using a variational model with set initial decomposition parameters, and traversing the signal analysis frequency band under the guidance of a central frequency convergence trend to iteratively decompose the dynamic signal x (t) to obtain an optimized mode {m1... Mn... MN} and a corresponding central frequency {omega1... Omegan... OmegaN}; (4) searching a fault correlation mode mI, guiding parameter optimization by the central frequency omega I of the fault correlation mode mI, and extracting an optimal target component containing fault information; and (5) performing envelope analysis on the optimal target component, and diagnosing the rotating mechanicalequipment according to an envelope spectrum. According to the fault diagnosis method provided by the invention, intelligent decomposition of the original dynamic signal of the diagnosis target equipment is realized by adopting a decomposition mode guided by a central frequency convergence trend, the acquired equipment dynamic signal can be adaptively analyzed, and the difficulty of performing mechanical fault diagnosis by a technician by using a variational mode decomposition method is reduced.

Description

technical field [0001] The invention belongs to the technical field of mechanical weak fault diagnosis, and relates to a fault diagnosis method under the action of central frequency convergence trend. Background technique [0002] Rotating mechanical equipment has been widely used in industrial production, and the status of mechanical parts directly affects the operating status and safety status of mechanical equipment. When a mechanical component fails, it will produce a periodic transient impulse response. How to effectively extract and accurately evaluate it is the key to bearing fault diagnosis. However, due to the complexity of the actual operating environment, the dynamic signal collected from the equipment site contains a lot of noise, and the weak fault characteristics of the signal are often submerged by the noise, which seriously affects the identification of fault characteristic signals. Therefore, it is of practical significance to carry out the extraction and d...

AI Technical Summary

Problems solved by technology

However, when using the variational mode decomposition method to process mechanical signals at present, it is difficult to predict the actual center frequency and the number of modal components in the original dynamic signal of the equipment, and it is difficult to completely extract the optimal balance parameters of the corresponding target components

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