Graphical-transformation-based RV speed reducer performance state monitoring method

Abstract

The invention relates to a graphical-transformation-based RV speed reducer performance state monitoring method. An AE (Acoustic Emission) signal is subjected to EMD (Empirical Mode Decomposition) andHLS (Hue, Light, Saturation) transformation to obtain a high-dimensional graphical transformation space for describing a state of an RV speed reducer. By describing the two-dimensional graphical transformation space, the graphical-transformation-based RV speed reducer performance state monitoring method can reflect the degradation degree of the mechanical property of the RV speed reducer, therebypreventing faults of the RV speed reducer and avoiding loss.

Description

technical field [0001] The present invention relates to the technical field of RV reducer mechanical performance state monitoring, in particular, relates to a graphical transformation-based RV reducer mechanical performance fault monitoring method. Background technique [0002] RV reducers are widely used in various mechanical equipment. When the mechanical performance of the RV reducer box decreases or fails, it will lead to a decrease in the accuracy of the mechanical system or even failure, resulting in major losses. Therefore, the mechanical performance status monitoring of RV reducer has very important engineering significance and application value. [0003] Vibration monitoring is one of the most common methods in machinery fault detection. Among them, acoustic emission shows great advantages due to its non-directional, early time and high frequency characteristics. In the traditional research on RV reducer mechanical degradation monitoring scheme through AE, thresh...

AI Technical Summary

Problems solved by technology

In the traditional research on RV reducer mechanical degradation monitoring scheme through AE, threshold-based denoising technology is incorporated into empirical mode decomposition, and multiple statistical time features are estimated from the obtained signals to realize the health of RV reducer Condition monitoring, which achieves reliable dedendum crack detection, but fails to show the effect of speed changes in the proposed failure indicators; bearing and RV reducer by estimating the kurtosis and crest factor of EMD time-domain AE signals state, but the method does not clearly explain the severity of the damage; the signal is decomposed into several frequency ranges, and the fault indicators are extracted from the energy thresholds of different frequency bands, but the extracted energy values ​​are directly used in the fault classification system to use other parameters processing, rather than prior analysis to extract fault indicators containing the fault detection information itself

[0004] The main disadvantage of these methods is related to the use of statistical time-based fault indicators, which directly affects the effectiveness of identifying failure modes hidden within dynamic signals.

Images