When the rolling bearing in bridge crane gets out of order and often accompanies with occurrence of nonlinear behaviours, its fault information is weak and it is difficult to extract fault features and to distinguish diverse failure modes. Kernel principal component analysis (KPCA) may realize nonlinear mapping to solve nonlinear problems. In the paper the particle swarm optimization （PSO）is applied to optimization of kernel function parameter to reduce its bind set-up. The optimal mathematical model of kernel parameters is constructed by means of thought of fisher discriminate functions .And then it is used to bridge crane rolling bearing simulated faults recognition. The simulation results show that KPCA optimized by PSO can effectively classify fault conditions of rolling bearing. It can be concluded that non-linear mapping capability of KPCA after its function parameter by PSO is greatly improved and the KPCA-PSO is very suit for slight and incipient mechanical fault condition recognition.
展开▼
