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The coal conveying system is the most important composition unit of continuous mining machine, the performance of coal conveying system plays a decisive role in production efficiency, this equipment is easy to cause the fault, in this paper, the faults often appeared in this equipment and the fault reasons are analyzed, genetic algorithm (GA) and particle swarm optimization (PSO) are taken as the optimization tool, and then combined with the method of BP neural network, the fault diagnosis of coal conveying system is researched. The analysis results show that, this method can make the BP neural network away from local minima effectively, the accuracy of the equipment is increased, and then the fault diagnosis of equipment can be carried out quickly, conveniently and accurately. Introduction Continuous coal mining machine is a combined unit with multiple functions, it is mostly applied in short wall mining and driving openings, it is widely applied in the coal mine and other mines mining, it has good application value and application prospect. The most important composition unit of continuous mining machine is the coal conveying system, the performance of coal conveying system plays a decisive role in production efficiency, this equipment is easy to cause the fault, so the convenient fault diagnosis method is very meaningful for improving the coal mining efficiency. The traditional fault diagnosis methods use the BP neural network method, and it has a number of weaknesses, in order to improve the accuracy of fault diagnosis, BP neural network is optimized, the most effective optimization method is GA-PSO hybrid algorithm, this algorithm has many advantages, there are significant advantages in terms of weight training and convergence speed, and it can effectively avoid the local minimum value. Therefore, in this paper, the fault diagnosis of coal conveying system uses the method of GA-PSO hybrid BP neural network for optimization. Fault analysis and detection method A coal transport system is driven by the hydraulic system, it is comprised with hydraulic lifting cylinder, swing hydraulic cylinder, the scraper chain and scraper and the unloading roller, the system composition structure is shown as Figure 1. Figure 1 Coal conveying system structure of the coal mining system A. Fault phenomenon and fault reasons Coal transport system will have the following some troubles: scraper chain has the low transmission speed, scraper chain has a transmission rate of the instability, unstable International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2015) © 2015. The authors Published by Atlantis Press 1462 transmission of scraper chain causes noise and unstable vibration. 4 X operating temperature of bottom shaft 1 is too high. 5 X operating temperature of unloading roller 5 is too high, 6 X scraper chain 4 tightness has problems, the system cannot move freely in the vertical direction, 8 X the system cannot move freely in horizontal direction. 9 X the circulating pipeline temperature of the system is too high, 10 X the circulation pipeline has leakage problem. After the analysis of the fault phenomena, the fault reasons can be expressed as follows: 1 Y the system of clutch slipping; 2 Y bottom shaft and sprocket of the system are damaged due to friction; 3 Y lack of lubricating oil in the bearing parts; 4 Y the system for bearing unloading lack of lubrication; 5 Y hydraulic cylinder with lifting effect appears the oil spill; 6 Y hydraulic cylinder with oscillating action appears the oil spill; 7 Y circulating pipeline of the system is damaged; 8 Y damage occurred in hydraulic cylinder control valve has the lifting function; 9 Y the system cannot run in the latter part; 10 Y the system cannot operate in normal in the tension position. 11 Y sealing effect of circulation pipeline is failure. 12 Y due to impurity, hydraulic pressure fluid is not pure. 13 Y volume of hydraulic pressure fluid is insufficient. 14 Y hydraulic cylinder control valve with oscillating action is damaged. B.Detection method In the coal conveying system, 1 66 M M information detection points are set, in each detection points, the sensors are installed, corresponding to each key point, signal are collected, it is shown in Figure 1. For the detection of fault signals 2 X , 3 X , and 4 X , the temperature sensor 1 M is installed in the system of bearing seat, temperature sensor 2 M is installed in the pipeline of the hydraulic system of lifting the cylinders for detecting the fault signals 7 X and 10 X . Pressure sensor 3 M is installed in the pipeline of the hydraulic cylinder swing of the system for detecting the fault signals 8 X and 10 X . Pressure sensor 4 M is installed in the in the t central of the system for detecting the fault signals 1 X , 3 X and 6 X , Pressure sensor 6 M is installed in the in the roller bearing seat of the system for detecting the fault signals 2 X , 3 X and 5 X . Mathematical model of fault diagnosis neural network The mathematical model is consisted of three layers of neural network. The three layer neural network has 14 neurons output layers and hidden layer with 10 neurons, and input layer with 10 neurons. The net input value from the input layer to the hidden layer can be calculated by formula: 10 1 1 1 j ij i j i n X ω θ = = − ∑ (j=1,2,...,10) (1) Where, 1 j θ , ij ω are weights and threshold from the input layer to the output layer, i X is the input fault values of the sample, net input value from the hidden layer to the output layer can be calculated by formula: 10 2 2 1 11 e jk k k n j j v n λ θ − = = − + ∑ (k =1,2,...,14) (2) Where, 2k θ and jk v are weights and threshold from the hidden layer to the output layer. λ is the speed coefficient, the value is 1. Output error of neural network can be calculated by the formula (3): 2 14 2 1 1 1 2 1 e k n k k E Y λ − =   = −   +   ∑ (3)

Coal Conveying Fault Diagnosis Design of Coal mining machine Based On Software Control