基于瞬态电机电流信号分析(TMCSA)的异步电机故障诊断具有重要科学意义和工程应用价值，是相关领域的热点研究问题之一。但目前该问题没有很好的解决方法，其中最主要的难点是TMCS中瞬时高频特性组分的准确提取。另一方面，Synchrosqueezed小波变换(SWT)正是为了精确分离和提取多组分信号中的瞬时频率特性而提出的一种自适应小波变换方法。本项目拟用SWT解决异步电机故障诊断中的瞬时高频特性有效识别难题，主要研究内容包括：SWT母小波和故障指标的选取、SWT算法的滤波器组实现、滤波器组实现模块的故障敏感性和噪声鲁棒性分析等等。项目课题组已有丰富的相关研究积累，拟研究问题创新性强，既有重要的科学意义，也有广泛的应用前景。

The diagnosis of induction motor faults is of great importance in both scientific research point of view and practical engineering applications. Among the diagnosis methods, the one based on the transient motor current signature analysis (TMCSA) have been drawn much attention and become one of the most important research topics. However there is still no effective solution so far. The most challenging problem is to precisely locate and extract the high frequency transient component in TMCS. On the other hand, Synchrosqueezed Wavelet Transform (SWT) is an adaptive wavelet transform method, by which the instantaneous frequency component can be extracted effectively from a multicomponent signal. In this research, SWT will be used to solve the challenging problem of characterizing the high frequency transient component aroused in fault diagnosis of induction motors. The proposed research includes the following problems: the selection of mother wavelets in SWT and the corresponding fault index, the SWT algorithm and its filter bank implementation, the fault sensitivity and noise robustness of the implemented filter bank module, etc. The project group has good track record in research. The proposed problems are novel, challenging and of great importance in theory and practical applications.