Failure diagnosis and health evaluation method based on wavelet power, manifold dimension reduction and dynamic time warping

Abstract

The invention discloses a failure diagnosis and health evaluation method based on wavelet power, manifold dimension reduction and dynamic time warping, and aims to improve the feature separability of bearing failure, impeller failure and the mixed failures of a centrifugal pump and realize diagnosis and health evaluation of various states. The method comprises the following steps: firstly, decomposing collected vibration signals of the centrifugal pump into 8 wavelet components by applying wavelet packet conversion; extracting wavelet energy of each component to be taken as a failure feature to obtain an eight-dimensional failure feature vector; then conducting dimension reduction on the eight-dimensional feature by applying a manifold learning method to obtain a three-dimensional feature vector with better separability, simplicity and stability; finally, based on the feature vector, measuring the distance of test data and training data by applying a dynamic time normalization method so as to determine the current failure state and realize failure diagnosis of a bearing. The distance value can also reflect the health degree of the current state, can realize evaluation of the health state of the centrifugal pump, and has the excellent practical engineering application value.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis and health assessment of centrifugal pumps, in particular to a fault diagnosis and health assessment method based on wavelet energy, manifold dimensionality reduction and dynamic time warping (DTW). Background technique [0002] Centrifugal pumps are widely used in electric power, petrochemical, metallurgy, machinery and other industrial sectors, and are key components that affect the normal operation of the system. Therefore, accurate diagnosis of centrifugal pump faults and effective assessment of the health status of centrifugal pumps are of great significance for the stable operation of industrial equipment. Since the centrifugal pump will vibrate during the rotation process, and the strength of the vibration signal generated in different states is also different. Therefore, the fault diagnosis and health assessment based on the vibration signal are widely used methods at present. In ...

AI Technical Summary

Problems solved by technology

However, the feature vectors extracted from this are often high-dimensional, which cannot effectively reflect the differences between the characteristics of each fault state, and the high-dimensional features directly used as input vectors for subsequent fault classification or health assessment algorithms will greatly increase their complexity. Therefore, it is necessary to reduce the dimensionality of high-dimensional features

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