测控技术2011，Vol.30Issue(12)：102-105,4.
基于粒子群神经网络的气阀机构故障诊断
Valve Train Fault Diagnosis Based on PSO Neural Network
摘要
Abstract
A novel fault diagnosis method for diesel valve train based on particle swarm optimization (PSO) neural network( NN) and wavelet packet decomposition is proposed. PSO algorithm is employed as learning algorithm of NN, to overcome drawbacks of pure BP algorithm. To avoid the slow search speed around global optimum in PSO algorithm, a heuristic way is adopted to give a transition from particle swarm search to gradient descending search. By simulating two kinds of main fault of a valve train, which are the gas leak and abnormal lash, the vibration signals of a cylinder head have been measured. The different frequency bands energy of diesel engine vibration signal after wavelet packet decomposition constitute the input vectors of PSO NN as feature vectors. Diesel valve faults are classified by using the PSO NN. A comparative experiment shows that the method has more fast convergence speed and higher diagnosis accuracy than BP algorithm. That also shows the correctness and validity of this method in diesel valve train fault diagnosis.关键词
柴油机/粒子群优化算法/神经网络/故障诊断Key words
diesel engine/ particle swarm optimization algorithm/ neural network/ fault diagnosis分类
能源科技引用本文复制引用
游张平,胡小平..基于粒子群神经网络的气阀机构故障诊断[J].测控技术,2011,30(12):102-105,4.基金项目
丽水学院重点科研项目(KZ201118) （KZ201118）
国家863计划资助项目(2008AA042803) （2008AA042803）
丽水学院引进人才科研启动基金项目(2009001) （2009001）
