基于粒子群神经网络的气阀机构故障诊断

提出应用粒子群神经网络和小波包能量特征的柴油机气阀机构故障诊断方法.为了克服BP算法的缺陷,将粒子群优化(PSO)算法应用于神经网络的学习算法中；为了避免PSO算法在全局最优值附近搜索变慢,采用了一种从PSO搜索到BP搜索的启发式算法；然后,通过模拟柴油机气阀机构的两种常见的主要故障:气阀漏气和气门间隙异常,采集气缸盖...

Valve Train Fault Diagnosis Based on PSO Neural Network

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.