中文摘要：

旋转设备的微弱故障特征信息提取对于故障的早期预警具有重要意义,连续小波可以通过变换对信号实现多尺度细化分析,能够在不同的尺度上描述信号的局部特征,因此有利于微弱故障信号的检测。不同尺度上的信号重构对于设备的故障特征表示并不相同,为此提出一种基于连续小波变换的微弱特征提取新方法。对信号采用连续小波进行分解,应用小波熵来选择最优的尺度进行信号重构,并对重构信号进行包络谱分析;根据提取的特征频率来确定故障的种类。通过仿真信号和滚动轴承故障信号的微弱特征提取进行方法的验证分析。研究表明基于连续小波最优尺度重构方法能够有效地对微弱特征进行提取。

英文摘要：

Weak fault feature extraction of rotating equipment is important for early fault warning. Continuous wavelet transform （CWT） can analyse signal in multiple scales, which can describe the local characteristics of the signal in different scales through translation. Therefore, it is convenient to the weak fault characteristic determination. As the reconstruction of different scale signals lead to different classification results, this research presents a new early fault diagnosis method based on CWT. CWT is carried on for signal decomposition. The optimal scale for signal analysis is selected based on wavelet entropy. Envelope spectrum analysis is applied to the reconstructed signal for feature extraction. The working condition can be classified based on characteristic frequency. Simulation and roiling element bearing signals are used to verify the effectiveness of this method. It can be concluded that this method is suitable for weak feature extraction based on this investi~,ation.