Bearing fault characteristic extracting method for redundantly lifting wavelet transform based on self-adaptive fitting

Abstract

The invention provides a bearing fault characteristic extracting method for lifting wavelet transform based on self-adaptive fitting. The bearing fault characteristic extracting method is used for diagnosing faults of a rolling bearing through a vibration acceleration signal, and comprises the following steps of: creating nine wavelets with different characteristics through calculation formulas and lifting algorithms based on data fitting; carrying out redundant lifting wavelet transform on a vibration signal by using the nine created wavelets in sequence; determining the optimum and abandoning the other eight in nine groups of decomposition results according to a normalized 1P norm value; analyzing segmented power spectrums of the initial vibration acceleration signal; selecting the optimum low-frequency approximation signal or a high-frequency detail signal for single reconstruction; subjecting the signal obtained by the single reconstruction to Hilbert demodulation; and judging running state of the rolling bearing according to frequency components in an enveloping spectrum. According to invention, early weak fault characteristic information of the rolling bearing can be extracted more effectively, and evidences are provided for state monitoring and fault diagnosis of the rolling bearing, so that accidents can be avoided as possible.

Description

technical field [0001] The invention relates to a feature extraction method of a rolling bearing, in particular to a bearing fault feature extraction method based on adaptive fitting redundancy lifting wavelet transform. Background technique [0002] Rolling bearings are one of the most frequently used components in modern production equipment. Failures caused by normal operation loss and improper operation will not only cause major economic losses to the enterprise, but may even lead to serious consequences of casualties. Therefore, it is of great significance to carry out condition monitoring on rolling bearings and effectively extract characteristic information that can reflect their operating conditions, so as to detect potential failures as early as possible and avoid accidents. [0003] The vibration signal of rolling bearings in industrial field usually has non-stationary characteristics. In order to effectively capture the fault characteristics in the vibration sign...

AI Technical Summary

Problems solved by technology

However, the classical wavelet needs to be realized by convolution operation, resulting in a large amount of calculation; at the same time, it is difficult to match various complex features in the vibration signal only through a certain wavelet

Although the

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