基于多维特征参数的装备状态评估方法

针对利用多维状态特征参数进行状态评估时存在的信息冗余和赋权难度大的问题，运用自组织特征映射网络（self-organizing feature map, SOM）的无监督聚类能力，对状态特征参数进行离散化处理；利用粗糙集属性约简算法剔除冗余特征参数；采用变尺度混沌优化算法对特征参数的权重进行优化。案例分析表明，该方法解决了状态评估多维参数权重的优化问题，提高了状态评估的准确性。

Equipment condition evaluation based on multi-dimensional characteristic parameters

Condition evaluation usually runs into excessive calculation and difficulty in weight determination. This problem is caused by information redundancy when the evaluation information is composed by multi-dimensional characteristic parameters. For this, the self-organizing feature map (SOM) network is opted to discretize parameters with continuous ranges, for the SOM network is capable of clustering without supervision. At the same time, the redundant algorithm of attributes based on rough set is used to eliminate redundant characteristic parameters. Mutative scale chaos optimization algorithm is utilized in optimization of weights for resulting parameters of reduction. The outcome of case study shows that the method applied is notably able to solve the optimization of weights for multi-dimensional parameters and enhance the accuracy of evaluation.