Agricultural machinery fault identification system

Abstract

A fault identification system for agricultural machinery, comprising the following modules: a) a monitoring module, which converts the operating status of agricultural machinery components into digital signals and stores them; Preset the operation parameter value, and compare the digital signal transmitted by the monitoring module with the preset operation parameter value; c) Judgment module, the judgment module judges whether the comparison result of the comparison module belongs to the category of mechanical failure; d) Cloud module, cloud The module uploads the judgment result of the judgment module to the cloud server, and searches for the most optimal fault solution according to the judgment result.

Description

technical field [0001] The invention relates to the field of machinery, in particular to an agricultural machinery fault identification system. Background technique [0002] At present, there are many mechanical fault diagnosis methods, including static-based mechanical fault diagnosis methods and dynamic mechanical fault diagnosis methods. Among them, the mechanical fault detection method that takes mechanical vibration as the detection object is currently a new method in the field of mechanical fault detection. Mainstream method. However, the accuracy of fault diagnosis and the type of fault identification based on mechanical vibration fault detection methods largely depend on the analysis and processing capabilities of mechanical vibration signals. At present, most of the existing mechanical fault diagnosis methods focus on the acquisition and analysis algorithms of mechanical vibration signals, which also include a large number of different transformations or signal con...

AI Technical Summary

Problems solved by technology

However, the current series of typical mechanical fault diagnosis methods, the fault time and diagnosis algorithm designed by them largely depend on statistical or probability analysis, and there is no clear basis for fault identification.

Because of this, the current mechanical fault detection methods have strong limitations, that is, for different scenarios and different detection objects, the ability and effect of fault detection often vary greatly.

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