Compressor surge early fault feature extraction method based onenhanced entropy weight

Abstract

The invention discloses a compressor surge early fault feature extraction method based on enhanced entropy weight dimensionality reduction, belongs to the technical field of mechanical equipment fault diagnosis, and can extract surge early-stage fault sensitive features more accurately and effectively. The method comprises the following steps: acquiring m groups of normal vibration data samples of a compressor to be monitored and fault vibration data samples of surge early faults, wherein the surge early failure refers to a rotating stall failure. respectively extracting n time domain and frequency domain feature parameters from the normal vibration data sample and the fault vibration data sample, and constructing a sample feature matrix; normalizing the sample feature matrix, and calculating enhanced information entropy values of the n feature parameters. based on the enhanced information entropy values of the feature parameters, measuring weight coefficients of the n feature parameters representing the surge early failure capability; and according to the size relationship of the weight coefficients, extracting the feature parameters of which the weight coefficients exceed a set threshold value as sensitive features of the surge early fault to be monitored.

Description

Technical field [0001] The present invention relates to the field of mechanical equipment fault diagnosis, and more particularly to an early fault feature extraction method for compressor surges based on enhanced entropy. Background technique [0002] Compressors are widely used in petrochemical, smelting, mechanical, power and other fields. In the actual operation, it is extremely vulnerable due to improper working conditions, environmental changes, and other factors, and a surge fault occurs. Once the breath fault occurs, it is often damaged by important components such as compressor spindle, impeller, bearing, seal, and the safety and stability of serious threat equipment. Therefore, how to quickly and accurately identify the asthma failure to become a key factor in maintaining a stable operation of the unit and ensuring the continuous production of the enterprise. [0003] Based on the maneuvement fault mechanism, the on-site personnel often determines based on the abnormal s...

AI Technical Summary

Problems solved by technology

[0004] There is overlap of fault information between multi-dimensional feature parameters, resulting in redundant information, which not only fails to improve the accuracy of fault identification, but also increases the complexity of the algorithm, and brings about the problem that the features cannot be visualized

At present, the commonly used feature selection method can play an ideal effect when there are obvious differences between the characteristics of different types of data, but the characteristics of the surge signal are weak in the early stage, coupled with the interference of vibration and noise, resulting in a gap between normal and fault states. The distinction degree of the characteristic parameter values ​​between is extremely small, and it is difficult to accurately select sensitive features by using the above feature selection method, which cannot be used for the extraction of early features of surge faults.

[0005] Therefore, there are still some deficiencies in the research on the early fault characteristics of compressor surge, and the problem of how to effectively extract the fault characteristics that accurately and timely reflect the compressor surge state needs to be solved urgently

Images