Rapid dictionary learning algorithm for sparse representation of mechanical vibration signals

Abstract

The invention belongs to the technical field of mechanical vibration signal processing. In order to solve the problem of long dictionary training time in a K-SVD algorithm, the invention discloses a rapid dictionary learning algorithm for sparse representation of mechanical vibration signals, and the algorithm specifically comprises the following steps: S1, selecting a training sample to determinean initial dictionary and determine the number ml of atoms of multiple columns of samples adjacent to the optimal time sequence; S2, performing synchronous sparse coding on multiple columns of sampleatoms adjacent to a training sample time sequence by adopting a synchronous orthogonal matching pursuit (SOMP) method to obtain a sparse coefficient matrix A; s3, fixing the sparse coefficient matrixafter synchronous sparse coding, and updating the dictionary by adopting a least square method (SGK); and S4, repeating the step S2 and the step S3 until an iteration stop condition is met, and completing dictionary training to obtain a learning dictionary. By adopting the dictionary learning algorithm provided by the invention, the dictionary training rate can be greatly and effectively improvedunder the condition of ensuring the compression and reconstruction performance of the vibration signal.

Description

technical field [0001] The invention belongs to the technical field of mechanical vibration signal processing, and in particular relates to a fast dictionary learning algorithm for sparse representation of mechanical vibration signals. Background technique [0002] For sensors based on the traditional Nyquist sampling theorem, when collecting mechanical vibration signals for real-time state monitoring and fault diagnosis analysis of mechanical equipment, the sampling frequency must be twice higher than the highest frequency of the vibration signal in order to accurately collect mechanical vibration signals . With the rapid development of modern industrial technology, the frequency band of vibration signals of modern mechanical equipment is getting wider and wider. For increasingly large-scale, integrated and intelligent mechanical equipment, the use of Nestle's theorem for signal acquisition will bring Massive data, especially in real-time monitoring of remote equipment, re...

AI Technical Summary

Problems solved by technology

[0005] However, in the dictionary training process of the K-SVD algorithm, the sparse coding phase uses the OMP algorithm to sparsely encode the training sample atoms column by column, and the dictionary update phase uses the SVD decomposition algorithm to update the dictionary atoms, so that the entire dictionary training It takes a long time, so there are still some limitations in real-time monitoring of mechanical equipment using this algorithm for compressed measurement and reconstruction. Therefore, it is necessary to optimize and shorten the dictionary training time

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