Research On Neural Network Based On Optimal Theory And Its Application On Pump-jackk Fault Diagnosis Of Oil Field

The key of pump-jack fault diagnosis is realizing the mapping from fault symptom space to fault space.It is a complex nonlinear problem and it can realize the identification and diagnosis of the fault.Neural network offer the way for fault diagnosis,because it have the ability of self-learning,nonlinear mapping,arbitrary function approximation,parallel computing and fault-tolerant. But because of complex working condition and various fault type,so when diagnose, existing problems such as network vast,long learning time and falling into local extreme value and so on.Neural network combining with genetic algorithm,evolutionism and other algorithm will become the trend of the fault diagnosis.In this paper, neural network and optimization algorithm are combined to improve the properties of neural network,so then used for pump-jack fault diagnosis.The details are follows:1,Design a double weights extension neural network for fault diagnosis.The inputs of the network are status data of the pump-jack and the outputs of the network are fault type. Inputs and outputs are connected by double weights,the weights are the upper and lower limit of the status data. Put forward adaptive ,it can change the crossover and mutation adaptively and it's evaluation function is extenics distance.Making use of the genetic algorithm's global search ability to optimize the neural network weights. Overcame the BP's disadvantage of premature convergence and falling into local extremum.2,Design an immune genetic neural network. Improve the immune genetic algorithm and give a calculational methods of affinity degree based on antibody vector distance. An adjusting factor based on density is increased in the process of promoting and suppressing of antibody. Thus, the best individual can be preserved, the diversity can be ensured, and the phenomenon of premature convergence can be avoided. Optimize the hide centers of RBF network by using the improved genetic immune algorithm. The characteristic of low learning efficiency of RBF neural network can be overcome.The approximation accuracy can be also improved and the number of constructing the center of the hide layer of network is dispensable.3,Design a particle-swarm-optimization-based neural network. The traditional PSO algorithm is easy to fall into local optimum situation in the later stage.In this paper the rate equation of the PSO is updated by introducing a minuscule disturbing term and a dynamic changing acceleration factor.The new PSO can adjust the PSO's performance in the earlier and later stage respectively.The new PSO algorithm is used to train the weights and thresholds of the BP network.4,Give a new fusion diagnosis method. Make a software package,it synthesize the neural netwok in the paper. Using wireless patrol data realize the fault diagnosis of pump-jack,analyze and contrast the result.