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柴油机以其良好的动力性、可靠性、经济性在农用机械中广泛应用,但对农用柴油机施行及时的不解体故障诊断却并非易事。为此,以配气机构故障为例,提出了将小波包分解与RBF神经网络结合的故障诊断方法,对降噪后的气缸盖振动信号进行小波包分解,构造故障特征向量作为故障样本,并用训练好的RBF神经网络进行模式识别。试验结果证明,该方法具有良好的诊断效果和广泛的工程应用前景。
Diesel engine with its good power, reliability, economy, widely used in agricultural machinery, but the timely implementation of agricultural diesel engine fault diagnosis is not easy. To solve this problem, a fault diagnosis method based on wavelet packet decomposition and RBF neural network is proposed, and wavelet packet decomposition of the vibration signal of the cylinder head after noise reduction is proposed. The fault eigenvector is constructed as a fault sample and used together Trained RBF neural network pattern recognition. Experimental results show that the method has good diagnostic results and a wide range of engineering applications.