1、Research on Fault Diagnosis of Fork Lift Truck Hydraulic System Based on Artificial Neural Network AbstractThe structure and algorithm of BP neural net were described, therealization process of the fault diagnosis of hydraulic system based on BP neural net was discussed. According to the experiment
2、and test of fault of fork lift truck hydraulic system, the BP net has better learning function, high net convergence rate and high stability of learning and memory. The diagnosis results indicate that the presented diagnosis method has high reliability and can attain the expected results, which can
3、be applied to fault diagnosis of hydraulic system.Keywords-Bp algorithm;Neural network;hydraulic system; fault diagnosis I. INTRODUCTION Because of the very complex structure of fork lift truck hydraulic system, once some faults happen in using process, it will have direct effect on operation effici
4、ency. Therefore, the reliability and maintainability of the fork lift truck hydraulic system become increasingly high. At present, the traditional method of maintenance mainly depends on peoples experience, and it is very difficult to guarantee quality and efficiency of maintenance. Due to its self-
5、organizing and nonlinearly adaptive nature, an artificial neural network potentially offers a new parallel processing paradigm that could be more robust and user-friendly than the traditional approaches. In fault diagnosis of hydraulic system, diagnosis information is acquired more easily by an arti
6、ficial neural network than a single expert system based on regulation speculation. This paper describes application of BP neural network in fault diagnosis of the fork lift truck hydraulic system, and provides a newly solution methods. II. A MODEL STRUCTURE OF BP NEURAL NETWORK AND TRAINING ALGORITH
7、M A. A model structure of BP neural network A typical structure of a three layer forward neural network is shown in figure 1. It includes input layer, hidden layer and output layer. In figure 1, circles represent neurons. Connecting line having weight between circles represents interaction strength
8、between neurons, where is the connection weight between neuron i in the k-th layer and neuron j in the k-1-th layer. is the threshold of neuron, (i=0n) is the input of neurons, (j=0m) is the output of neurons, and F() is a transfer function from the (k-1)-th layer to the k-th layer. B. Learning algo
9、rithm of BP neural network BP (Back propagation) neural network uses the error of the output layer to estimate the error of the direct precursor layer of the output layer, and then use the error to estimate the error of the preceding layer again and again. The estimates of error of the other layers
10、again and again. The estimation of error of the other layers can be obtained. In this way, it may form the process that transmits the error of the output layer to the input layer of network along the transmission right about of the input signals. Thereby, the algorithm is called the Back Propagation
11、 algorithm. And the non-cycle network that uses the BP algorithm to learn is called BP network. Its course of learning is just the course of training. The training is to adjust the weights among neurons by certain manner when the samples vectors are put into neural network. The specific realizations
12、 of BP learning algorithm follow as: Initialize right aggregate wij, get the value of the lesser stochastic nonzero; Give many pairs of input and output samples (Xp, Dp), where p=1, 2, , p, i is number of training mode pairs; Xp is input vectors, Dp is output expectation vectors. Calculate their act
13、ual output Yp=(y1p, y2p, , ymp), in this course, many times of positive spread calculation is done in terms of the different number of network layer. Evaluate the objective function of the network, and the output error value can generally be denoted as: Judge whether the network satisfies the precis
14、ion Where is the desired precise, the process of training will continue until the precision is attained. Adjusting the weights through dropping off one by one along the reverse according to grads can be computed by:III. ESTABLISHING BP NEURAL NETWORK OF FAULT DIAGNOSIS OF HYDRAULIC SYSTEM This paper
15、 is type of CPQ30 fork lift truck as a example. Fault rate of hydraulic system ismuch higher, and many fault reasons also occur. Aimed at general fault of hydraulic system,BP neural network verifies fault reason. A. Analyzing fault mode and fault mechanism ofhydraulic system Analysis of fault mode a
16、nd fault mechanism of hydraulic system isshown in table 1. B. Selecting the input and output vector of BP neural network Units We consider fault mode x=(x1, x2, x3) as the input vector of neural network, and fault reason y=(y1, y2, y3, , y7) asthe output vector of neural network. The nonlinear, mapping relation between fault mode and fault mechanism is established, Then
