Klingelnberg bevel gear fault diagnosis method based on sensitive IMF (instinct mode function) components

Abstract

The invention discloses a Klingelnberg bevel gear fault diagnosis method based on sensitive IMF (instinct mode function) components. The method includes 1, measuring a Klingelnberg bevel gear box by an acceleration sensor, collecting acceleration vibration signals, 2, importing the collected signals into Matlab to acquire initial signals, performing EMD (empirical mode decomposition) on the initial signals to acquire a series of IMF components, 3, calculating sensitiveness of each IMF component according to the sensitiveness evaluation algorithm, selecting the sensitive IMF component, 4, calculating instant energy spectrum of the sensitive IMF component, drawing a instant energy spectrogram, and accurately extracting fault characteristics according to the amplitude distribution of the spectrogram. The method is an effective fault characteristic extracting method, and can be applied to fault diagnosis on a Klingelnberg bevel gear; fault information can be quickly and accurately extracted; and a significant theoretical basis is provided for the fault diagnosis and the characteristic extraction of the Klingelnberg bevel gear.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis, relates to a fault diagnosis method for a Klingenberg bevel gear, and more particularly relates to a fault diagnosis method for a Klingenberg bevel gear based on a sensitive IMF. Background technique [0002] Klingelnberg Spiral Bevel Gear, as one of the two tooth systems of spiral bevel gears, has the characteristics of smooth transmission, high bearing capacity, hard tooth surface scraping technology, etc., so it is especially suitable for high power and high torque In the field of heavy-duty transmission, it is the core transmission component in important fields such as heavy-duty high-end CNC machine tools, automobile transmission systems, and aerospace equipment. This kind of gear usually works under complex working conditions such as heavy load, impact and variable load, and often causes pitting, gluing, cracking and even broken teeth, which leads to gear failure. Failure to detect...

AI Technical Summary

Problems solved by technology

However, how to effectively extract the fault information from the vibration signal of the Klingenberg bevel gear, the current mechanical equipment fault diagnosis technology still fails to meet the actual needs, and still has great research potential. The diagnostic methods and techniques are still one of the important research contents in this field

Images