Abstract
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.
Highlights
Rolling bearing is very important component of drive system in bridge crane. When it gets out of order, its fault information is weak and it often accompanies with occurrence of nonlinear behaviours, so it is difficult to extract fault features and to distinguish diverse failure modes, and it is disadvantage of the recognition and diagnosis of fault condition
To given data sets, the classification of Kernel principal component analysis (KPCA) is influenced by itself parameter of kernel function, it is of utmost importance to choose optimum kernel function parameter
The nonlinear mapping is realized by inner product operation, which is only required kernel function calculating for inner product in the original space, and without concerning specific form of nonlinear mapping, so called as KPCA
Summary
Rolling bearing is very important component of drive system in bridge crane When it gets out of order, its fault information is weak and it often accompanies with occurrence of nonlinear behaviours, so it is difficult to extract fault features and to distinguish diverse failure modes, and it is disadvantage of the recognition and diagnosis of fault condition. To given data sets, the classification of KPCA is influenced by itself parameter of kernel function, it is of utmost importance to choose optimum kernel function parameter (namely Kernel parameter) It mainly relies on a great deal number of experiments for sure or uses method of crossing tests, which is timeconsuming and low efficiency, and fails to make sure the kernel function is in optimal parameters. At last the recognition efficiency by PSOKPCA will be analyzed
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