SVM fault diagnosis method based on NMF

For overcoming the difficulty of fault feature extraction and solving the low efficiency of fault feature classification in a large dimensions fault diagnosis system,an algorithm of support vector machine(SVM)based on non-negative matrix factorization(NMF)fault diagnosis was researched. It is to avoid the direct feature selection and extraction, to reduce the characteristic dimension,and improve the high-dimensional data feature mode classification speed and accuracy. In order to avoid NMF randomness,characteristics of fault samples dimensionality reduction by training samples coefficient matrix was calculated, so that the consistency of the scale of NMF decomposition times was ensured. The experiment shows that this algorithm can reduce the dimensions of fault feature. The method can enhance the running efficiency and the estimating accuracy.