Mechanical critical component virtual degradation index construction method based on distance metric learning

Abstract

The invention provides a mechanical critical component virtual degradation index construction method based on distance metric learning. First, time domain, frequency domain and time-frequency domain characteristics of a vibration signal of a mechanical critical component are extracted, dividing a degradation state of the mechanical critical component according to a frequency spectrum and a power spectrum, next, the relevance, monotonicity and predictability of each index are evaluated comprehensively, indices to which performance correspond is superior to root-mean-square values are selected to form feature vectors of the mechanical critical component are selected and distance metric learning is performed, then the feature vectors in a normal state are utilized to train an optimized self-organizing mapping neural network, inputting newly acquired vibration signal data, and calculating the distance between the eigenvectors and weight vectors of corresponding activation nodes, thereby establishing virtual degradation indices with enhanced minimum quantization error. The mechanical critical component virtual degradation index construction method based on distance metric learning synthesizes various physical degradation indices of multiple domains, can fully mine the degradation information of a critical component of mechanical equipment, and is beneficial to improving the accuracyof residual life prediction.

Description

technical field [0001] The invention belongs to the technical field of remaining life prediction and health management of mechanical equipment, and in particular relates to a method for constructing virtual degradation indicators of key mechanical components based on distance metric learning. Background technique [0002] Mechanical equipment often works in a complex and changeable environment, and its key components fail frequently. With the development of modern technology, the coupling relationship between key components of mechanical equipment is getting closer and closer. Once a component fails, it will cause the entire The failure or even paralysis of the mechanical system will cause serious economic losses and even casualties. Therefore, it is imminent to predict the remaining life of key mechanical components so that they can be maintained before failure and ensure the safe service of mechanical equipment. [0003] The remaining life prediction of key components of ...

AI Technical Summary

Problems solved by technology

Excellent degradation indicators need to have good correlation, monotonicity and predictability, but affected by the quality of the original signal and signal processing methods, the physical degradation indicators directly extracted from the monitoring signal are often only valid for a certain stage of the degradation process. More sensitive, difficult to maintain a good trend throughout the degradation process

At the same time, the working environment of mechanical equipment is complex and changeable, and the physical degradation index is greatly affected by the working conditions, which is not conducive to the expression of degradation information of key mechanical components

The above shortcomings will lead to a reduction in the accuracy of early health monitoring and remaining life of key mechanical components

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