Abstract: According to the fact that current condition evaluation models lay more emphasis on the static observations of characteristic parameters and less on trend information inherent in sequential observations, a new condition evaluation model with static and dynamic evaluations is proposed. For the weighting of multi-dimensional characteristic parameters, a mutative scale chaos algorithm is applied to achieve optimal objective weighting, and a static condition evaluation model is established. Based on static evaluation, a “distance” which measures real-time deteriorating speed and standard deteriorating speed is opted to modify the static evaluation result. NSD-WKFCM (neighbor sample density weighted kernel fuzzy C-means) clustering algorithm is used to solve standard vector of deteriorating speed. A dynamic adjusting function based on “distance” combined with its parameter optimization algorithm is proposed, and the complete dynamic condition evaluation model is established. A case study is performed to verify the effectiveness of the model.

Key words: ordnance science and technology, multi-dimensional characteristic parameter, chaos optimization algorithm, NSD-WKFCM clustering algorithm, dynamic evaluation