Mechanical device vibration signal monitoring adaptive variable sampling method and device

Abstract

The embodiment of the invention provides a mechanical device vibration signal monitoring adaptive variable sampling method and device. The method comprises the steps of: collecting vibration signals of a mechanical device; performing time-domain analysis and frequency-domain analysis of the vibration signals to correspondingly confirm whether the vibration severity and a fault feature frequency amplitude of the mechanical device are abnormal or not; if yes, adaptively improving a sampling rate of the next sampling; or else, adaptively reducing the sampling rate of the next sampling. Accordingto the embodiment of the invention, the mechanical device vibration monitoring system energy consumption and data bulk can be reduced, and the universality is good.

Description

technical field [0001] The present application relates to the technical field of mechanical equipment vibration monitoring, and in particular, to an adaptive variable sampling method and device for mechanical equipment vibration signal monitoring. Background technique [0002] In traditional mechanical equipment vibration signal acquisition, a fixed sampling rate is usually set at the highest possible fault frequency according to Nyquist's law. However, under normal conditions, the vibration frequency of mechanical equipment is low, and an excessively high sampling rate will increase data redundancy and increase the energy consumption of the acquisition system. This limitation is especially reflected in wireless sensor networks that require very strict energy consumption control. In sensor nodes, the energy consumption of data transmission is much greater than that of data processing. Therefore, the energy consumption of sensor networks with limited bandwidth can be reduced...

AI Technical Summary

Problems solved by technology

However, these methods do not belong to the field of self-adaptation, and this method has very strict requirements on the design of the peripheral hardware circuit of the acquisition terminal system, and it is difficult to realize

[0004] In the field of adaptive data acquisition, HUANG Ru and Zheng Yang et al. designed an adaptive sampling based on a time series prediction model, which uses prior information to continuously adjust the sampling rate. This method is more suitable for occasions where the signal changes slowly, and is more versatile. Difference

Z.L.Wang designed an adaptive data acquisition method for the distributed temperature measurement system based on Raman scattering, which changes sampling as the temperature distribution of the optical fiber changes. This method is only applicable to this measurement system, which greatly limits its versatility.

Images