局域波法和独立成分分析在转子系统故障诊断上的应用

提出了一种局域波法和独立成分分析相结合的机械故障诊断方法。对于机械设备的振动信号，通过局域波法得到的局域波时频图像能够同时在时间和频率上反映信号能量的变化。不同故障信号的局域波时频图像也明显不同，因此可以用来进行故障的判别。为了实现故障的自动分类，应用独立成分分析对不同故障信号的局域波时频图像进行独立分量分离，得到一组源图像的投影系数。在此基础上，利用概率神经网络实现故障的自动分类。以转子的早期摩擦，基座松动，不对中故障信号为例，应用该方法进行了研究，实验结果证明了该方法具有一定的实用性和有效性。

LOCAL WAVE METHOD & INDEPENDENT COMPONENT ANALYSIS AND ITS APPLICATION FOR ROTOR SYSTEM IN FAULT DIAGNOSIS

A mechanical fault diagnosis method is proposed combining local wave method and independent component analysis. For the plant vibration signal, the local wave time-frequency image can reflect the energy change in time and frequency axis. For different fault signals, the time-frequency images are distinctly different. So it can be used for fault detection. To realize automatic classification for different fault modes, the independent component analysis is applied to extract the independent source images,then the projection coefficients of source images can be obtained. The Probabilistic Neural Network is used to classify for the projection coefficients. The rotor system fault signals (early rub, loose, misalignment) are investigated using the proposed methods. The result shows that this method is, to a certain effect, effective and practical.