汽车起重机广泛应用于基础建设项目，工作环境恶劣，是事故率最高的特种机械设备之一。柱塞泵是起重机液压系统的关键部件，及时、准确地对其故障进行诊断是设备安全可靠运行的重要保障，而提取有效反映设备运行状态的特征和建立适当的故障诊断方法是设备安全可靠运行的关键问题之一。本课题以汽车起重机的振动信号为研究对象，鉴于其典型的非平稳、非线性和周期性特点，建立了基于相关峭度的特征提取和增强模型。在利用相关峭度分析刻画信号局部特征的同时，针对故障信号的微弱性，结合机械设备故障信息的先验知识，提出了约束独立成分分析（ICA）方法以实现故障信号提取，开发基于相关峭度和约束独立成分分析的特征增强算法，消除特征中的冗余信息，突出有效特征。为提高汽车起重机柱塞泵故障诊断的水平提供进一步的理论和技术支持。

Truck cranes are widely used in numerous infrastructure projects. Because of the severe working condition, truck crane is one type of special mechanical equipment with a highest accident rate. Plunger pump is a key assembly of truck crane, and dealing with the equipment breakdown promptly and accurately is very important in terms of reliability and downtime decreasing. Extracting the features relevant to the equipment conditions from mechanical signals including abundant running information is crucial to fault diagnosis. This project takes the vibration signal of the plunger pump in truck crane as study object. In view of the typical non-stationary, nonlinear and periodic characteristics, a model of feature extraction and feature enhance based on relevant kurtosis is presented. Based on the capacity of intensive depiction of signals' local characteristics, and the fault diagnosis prior knowledge of mechanical equipment, a constrained dependent component analysis method is proposed to extract the fault signals in according to the weak property of the fault signal. Some feature-enhancement algorithms based correlated kurtosis and constrained dependent component analysis will be proposed. The redundant information in the feature space is removed effectively, and the constructive information is stand out. This research might provide theoretical proposals and technology supports for improving the precision of fault diagnosis for plunger pump in truck crane industry.