Research On Fault Diagnosis Technology Of Shearer Cutting Unit Based On BP Neural Network

Shearer is a main equipment in the comprehensive mechanized of coal mine.The high power,high reliability,high intelligence and easy maintenance are the trend of the study of shearers nowadays,therefore,it is the research tendency.However,as the weakest link in the reliability of shearers,the cutting gear box has become the main factor that affects the operation of shearer.At present,the manufacturer of shearers is difficult to make a judgment according to the state monitoring method of the traditional cutting gear box,sometimes even misjudgment due to various factors,and bringing the wrong judgment to the coal mine production and equipment maintenance.Therefore,in order to the maintenance of the equipment,it provide a scientific decision support,and a simple and rapid monitoring and diagnosis technology is studied.This paper is based on querying a large number of related reference,analyzing the background,significance and research status.The features of the fault diagnosis of shearer cutting gear box and the shortcomings of the existing methods are described.Then the structure of the cutting section and the operating conditions of the shearer are introduced.This paper studies the vibration failure mechanism of gearbox,and combines the modern signal analysis technology and the statistics of the failure rule of rocker arm gearbox.Aiming at the gear and bearing faults,a BP neural network method is applied to carry out the fault diagnosis plan.It simulates the 8 types of failure types of loading test conditions in the rocker arm test rig of shearer,collecting vibration parameters of shearer rocker arm through the sensor and extracting 6 main sensitive characteristic value as the input of the neural network.The experimental results show that the BP neural network diagnosis scheme achieves the multi fault classification of gearbox gear and bearing,and achieves good classification results.