Data-driven fault prediction and anomaly measurement for complex systems using support vector probability density estimation

To quantitatively monitor the state of complex system, a data-driven fault prediction and anomaly degree measurement method based on probability density estimation is studied in this paper. First, an anomaly index is introduced and defined to measure the anomaly degree of samples. Then By improving the form of constraint condition, a single slack factor multiple kernel support vector machine probability density estimation model is presented. As a result, the scale of object function and the solution number are all reduced, and the computational efficiency of the presented model is greatly enhanced. On the other hand, as the introduction of multiple kernel functions, a multiple kernel matrix with better data mapping performance is obtained, which can well solve the composite probability density estimation for uncoupled data. The simulation test shows that the presented model has higher estimation precision and speed. The experiments on complex system fault prediction also show that the system's anomaly degree can be quantitatively and accurately measured by the anomaly index gained from the prediction results, which can effectively improve the fault prediction precision and increase the prediction advances.