Optimized kernel principal component analysis fault monitoring method based on chaotic particle swarm

The invention discloses an optimization kernel principal component analysis fault monitoring method based on a chaotic particle swarm. The method comprises the steps of obtaining an initial data matrix; wherein the initial data matrix comprises a normal data sample matrix and a fault data sample matrix; mapping the initial data space into an implicit feature space through nonlinear mapping, and performing nonlinear feature transformation in the implicit feature space; establishing a fault monitoring model by taking the initial data matrix as training data; acquiring test data; and inputting the test data into the fault monitoring model, and carrying out fault on-line monitoring on the test data. Kernel function parameters of kernel principal component analysis are optimized through a chaotic particle swarm optimization algorithm to find out the optimal nonlinear characteristics and accurately monitor nonlinear faults, so that the monitoring delay time is shortened, and the fault monitoring precision is improv