Software design of rotating machinery fault diagnosis system based on deep learning

Xiaofeng He,Xiulian Lu,Xiaofeng Liu,Yunxiang Ma,Lipeng He,Jiasheng Zhang,Tao Yang,S Trukhanov,C.-W Chow

doi:10.1051/e3sconf/202126003006

Xiaofeng He, Xiulian Lu + Show 7 more

Open Access

https://doi.org/10.1051/e3sconf/202126003006

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Abstract

With the development of Industry 4.0, in order to meet the needs of intelligent fault diagnosis of rotating machinery in the industrial field, this paper developed a fault diagnosis system for rotating machinery based on deep learning and wavelet transform methods. The system is based on the Python language and mainly combines the PyQt graphical interface framework and the TensorFlow machine learning framework to complete the training requirements for historical or online fault data, and perform online monitoring and diagnosis of equipment operating conditions. The diagnostic accuracy of the system test results is more than 95%, the software interface is friendly, the algorithm generalization ability is good, and the reliability is strong. It provides guidance for the diagnosis of rotating machinery.

Highlights

Large-scale rotating machinery is a key equipment in industrial production, which is widely used in power, metallurgy, and transportation
With the popularization of online monitoring systems, a large amount of online monitoring data has been accumulated in the industry, among which vibration signals are widely used in fault diagnosis of rotating machinery [2]
In order to solve the needs of intelligent fault diagnosis of rotating machinery in the industrial field, this paper is based on the Python language, mainly combines the PyQt graphical interface framework and the TensorFlow2.0 machine learning framework to build a fault diagnosis system, realizes the training of the historical operation data of the equipment, and carries out the operation status of the equipment

Summary

Introduction

Large-scale rotating machinery is a key equipment in industrial production, which is widely used in power, metallurgy, and transportation. Xiong et al [4] proposed a deep belief network (DBN) and particle swarming support vector machine (PSO-SVM) rotating machinery fault diagnosis method, which improves the diagnosis accuracy and shortens the training time. Zhang J H et al [5] proposed a new method of deep learning fault diagnosis based on clustering and stacked autoencoder, which makes the diagnosis more intelligent. In order to solve the needs of intelligent fault diagnosis of rotating machinery in the industrial field, this paper is based on the Python language, mainly combines the PyQt graphical interface framework and the TensorFlow2.0 machine learning framework to build a fault diagnosis system, realizes the training of the historical operation data of the equipment, and carries out the operation status of the equipment. And accurate diagnosis, providing data pre-processing, multiple depth model selection, manual adjustment of training parameters, multiple diagnosis methods and other functions

Main functional structure

Software design process

Software design and implementation

Application module design

Findings

Conclusions