Fault Diagnosis of Multivariable Dynamic System Based on Nonlinear Spectrum and Support Vector Machine

Fault diagnosis of multivariable dynamic systems is studied by combining nonlinear spectrum feature with support vector machine. In order to resolve the problem of large calculated amount of solving nonlinear spectrum, a frequency domain variable step size normalized LMS adaptive algorithm is proposed based on the one-dimensional nonlinear output frequency response function (NOFRF). The step size is updated in real time according to the spectrum estimation error and the previous step size. After obtaining nonlinear spectrum data, kernel principal component analysis is used to compress data and extract spectrum feature. In order to improve fault recognition precision, a multi-feature fusion SVM fault classifier is established based on different frequency domain scales. Every sub-classifier is constructed by the spectrum feature of each order, and the diagnosis result can be obtained by weighed fusion of all sub-classifiers. Consider the difference of classification reliability for input features, sub-classifier weight is obtained using the distance between input and SVM separating hyperplane. Simulation experiments indicate that the proposed fault diagnosis method has good real-time performance and high recognition rate, so it can meet the requirements of online diagnosis of multivariable dynamic system.