Abstract: This paper begins with the analyzing of existing methods for fault diagnosis and their advantages and disadvantages.Then it designs the process of loading machine fault diagnosis and gives the functions and detail designs of some important components in the process.It extracts fault features based on analysis of the loading machine signal.After that it founds two neural network models.One is BP neural network model，the other is assembled neural network model.Compared with the performance of the two models，this paper selects the fit one for loading machine fault diagnosis.

摘要： 首先分析了故障诊断的常用方法及其优缺点，设计了装载机故障诊断的流程，并阐述了流程中一些重要环节的设计和功能。然后在分析装载机信号的基础上提取了装载机信号的故障特征，相继建立了用于装载机故障诊断的BP神经网络和组合神经网络模型，并比较两者的优缺，选择更适合装载机故障诊断的模型。