Machine equipment fault diagnosis method based on artificial experience and voice recognition

Abstract

The invention discloses a machine equipment fault diagnosis method based on artificial experience and voice recognition. Sound signals of machine equipment are collected through a sensor, a sound sample library is formed through human marking, and then the sound signals are sent into a preset neural network model for training after data processing. The sensor collects the sound of the machine equipment in real time, inputs the sound into the trained neural network model after data processing, remotely recognizes the state of the machine equipment through the neural network, carries out the comprehensive judgment of a recognition result according to the artificial experience, and feeds back the result to the sound sample library. According to the machine equipment fault diagnosis method based on artificial experience and voice recognition, machine faults can be remotely diagnosed, and meanwhile the service life of key parts of machine equipment can be predicted; in addition, the neuralnetwork model is trained by adopting a deep learning algorithm in combination with artificial experience, so that the method has the advantages of high recognition accuracy, good safety, high efficiency, intelligence and the like.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of sound signals, in particular to a machine equipment fault diagnosis method based on human experience and sound recognition. Background technique [0002] At present, during the use of machinery and equipment in the factory environment, due to the influence of natural factors such as temperature, humidity, geographical location and human factors, the machinery and equipment are prone to wear and aging and many other problems. Fault diagnosis of machinery and equipment is a very complicated process of finding the cause from the phenomenon. Although there are many researches on the fault diagnosis of machinery and equipment at present, due to the many types of faults, the occurrence of faults is accidental or random. Due to the complexity, the fault diagnosis and cause mining of machinery and equipment are still a problem to be broken through. [0003] At present, according to the charact...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to provide a machine equipment fault diagnosis method based on manual experience and voice recognition for the machine equipment on the factory assembly line, so as to make up for the shortcomings of traditional machine equipment fault identification

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