An acoustic vibration signal processing method and system based on an environmental sample-based cloud model

Abstract

The present invention provides an acoustic-vibration signal processing method and system based on the environment sample-based cloud model, including collecting the acoustic-vibration signal in the environment as the original environmental signal, and using the time-frequency analysis method to preprocess the original environmental signal; The clustering method distinguishes the environmental signal and the sudden noise signal in the preprocessed environmental original signal, constructs the environmental sample cloud, and generates the environmental sample base cloud through multiple extraction and calculation intersections; collects the original signal containing the target signal, The cloud model is generated by the class method, and then the comparative analysis is carried out based on the cloud model state matching method, and the target signal, the environmental base and the sudden noise are separated to establish a new target feature cloud; The ideal cloud performs cloud model state matching with the target characteristic cloud to determine the state type of the target signal. It can effectively reduce external noise and significantly improve the accuracy of monitoring the state of the acoustic vibration signal of the target signal.

Description

technical field [0001] The present invention relates to the technical field of sound signal processing, in particular to a sound vibration signal processing method and system based on an environmental sample base cloud model. Background technique [0002] Acoustic vibration signal processing is a commonly used method in condition monitoring, and it is widely used in condition monitoring of electromechanical equipment and buried pipelines. Traditional acoustic and vibration signal processing methods often take the target signal collected in an ideal environment as a sample, and process the signal through comparison and analysis methods, which have the problems of low accuracy, single function, complicated operation, and unsatisfactory noise reduction. . In actual operation, it is often impossible to effectively extract the target signal. In a noisy environment, especially in a strong noise environment, the error tolerance rate is low, and it can only give a vague reference a...

AI Technical Summary

Problems solved by technology

Traditional acoustic and vibration signal processing methods often take the target signal collected in an ideal environment as a sample, and process the signal through comparison and analysis methods, which have the problems of low accuracy, single function, complicated operation, and unsatisfactory noise reduction.

In actual operation, it is often impossible to effectively extract the target signal. In a noisy environment, especially in a strong noise environment, the error tolerance rate is low. It can only give a vague reference and cannot be the core tool for signal monitoring.

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