The Research On Intelligent Fault Diagnosis Of Screw Conveyor Motor In Concrete Mixing Plant

After the introduction of the environmental protection tax law of the people's Republic of China and other relevant laws and regulations,many small concrete manufacturers and a large number of simple concrete production equipment has been phased out.The concrete mixing plant with larger scale,higher automation and integration has become the mainstream development direction.As one of the most important material conveying equipments in the mixing station,the spiral conveyor is difficult to guarantee the reliability and stability,because of its poor working environment and high working intensity.In this paper,the intelligent diagnosis method of spiral conveyor is studied by test method.Firstly,according to its actual working needs,the motor vector control model is established by using MATLAB/Simulink.Nextly,the main fault forms of the motor are simulated and analyzed.According to the characteristics of the motor fault signal,the wavelet decomposition method is proposed to extract the fault characteristic parameters of the motor.Then PCA(principal component analysis)method is used to select the main fault characteristic parameters of the motor and determine the fault samples.Finally,combining the advantages of BP neural network and genetic algorithm,the AGA-BP neural network fault diagnosis model is established,and the sample data collected are used to verify the test.The test results show that the diagnosis accuracy of the model is 73.3%,which indicates that the model designed in this paper has good fault diagnosis ability.Through this study,it provides effective technical support for the design of intelligent control system of concrete mixing station in the future.