Research On The Fault Prediction Technology Of Belt Conveyor In Coal Mine

As the core transport equipment in underground coal mine,belt conveyor is very important for its stable and efficient operation.At present,the health management of coal mine belt conveyor mainly depends on manual regular inspection and real-time fault diagnosis.Aiming at the problem of insufficient fault prevention ability of coal mine belt conveyor at present,based on the data analysis and the prediction method of neural network model,this paper attempts to predict the state parameters and identify the fault types of coal mine belt conveyor.The main work and innovation points of this paper include:(1)The basic structure and failure mechanism of the belt conveyor in ZhaoGu No.1 mine are analyzed.Taking the transmission system as an example,the causes of four faults of the equipment were studied and the corresponding monitoring parameters were screened.The fault data information base of coal mine belt conveyor is established.Aiming at the problem that the factors affecting the transmission system fault are various and large in number,the wavelet threshold and association rule algorithm are adopted to reduce the noise and dimension,and the effective fault information is mined to improve the data density.(2)Aiming at the weak prediction function of current coal mine belt conveyor management system,a fault prediction model based on DPNN neural network was established.In order to improve the accuracy and adaptive ability of model prediction,limit learning and PSO-LM combined learning algorithm were adopted for optimization.The model based on this algorithm is compared with the prediction model optimized by PSO-LM algorithm,PSO algorithm and LM algorithm by simulation,so as to verify the effectiveness of the prediction method proposed in this paper.(3)Based on the RBF neural network,the fault classification model of coal mine belt conveyor is established.Compared with the traditional BP neural network model,the superiority of RBF neural network is verified by experimental simulation.Combined with the discrete process neural network,and combined with the actual data to simulate the fault prediction of coal belt conveyor,the reliability of the combined model is verified.(4)According to the actual working condition and prediction requirement,thecorresponding functional modules are selected and the hardware structure of the prediction system is designed.Based on DPNN-RBF combined neural network prediction model,C# and MATLAB mixed programming were used to develop the prediction model.The system realizes the function of real-time detection and fault prediction of the conveyor state,and the software system runs stably with friendly interface and is easy to control.In this paper,the fault prediction method based on neural network is applied to the belt conveyor of coal mine.The fault prediction system established in this paper has practical application value,and the research model established in this paper can provide reference for the study of fault prediction in other fields.