运用小波变换检测汽车后桥总成故障

用傅里叶变换和小波变换寻找并分解出最能反映汽车后桥故障的频率段，再从该频率段的重建信号中提取最能反映故障的特征（包括信号的方差和峰态）。为了寻找最优判断故障的指标，从现场采集的后桥总成振动信号数据库中抽取一定样本的正常件和故障件，并提取每个工件信号的特征数据，用支持向量机神经网络可得到最优的分类指标。

Fault Detection of Automobile Rear Drive Assembly by Using Wavelet Decomposition

An on line fault detection algorithm for automobile rear drive asse m bly (RDA) using wavelet decomposition was proposed. The fast Fourier transform ( FFT) and the wavelet decomposition were used to decompose and find the frequency band which mostly represents the RDA faults, and then the most indicative featu res from the reconstructed signal of the frequency band, including the variance and kurtosis of the signal, were extracted. To find an optimal fault detection i ndex, a sample of RDA workpiece, including normal and abnormal ones, was randoml y selected from the vibration signal database which was gathered from the assemb ly line of RDA. The features of every signal were extracted and used to train th e support vector machine (SVM) neural network to find the optimal classification hyperplane. The algorithm is currently used on the assembly line. It is found t hat the algorithm is robust to different working conditions, and is able to dete ct the faults in RDA online and effectively.