PCA and Local-Wave Method Analysis on Fault Diagnosis of Diesel

Extracting features from the vibration signals has been recognized to be a difficult issue, essentially because of the strong nonlinearity and nonstationary of the signals. In this paper, local wave method is combined with principal component analysis (PCA) and nonlinear dynamics as a model of feature extraction. In this model, reconstruction theory was used to extract dynamic space from time series, PCA was applied to reduce the dimension of the space and make the fault information clearly. In the end, an example of practical application shows that the dimension of the space of the vibration signal of 6BB1 diesel engine is reduced and the fault information is made clear by using the model above, the practicality is explained in reason. The example prove that this integrated method is feasible.