基于模糊聚类算法的船舶故障诊断技术

由于大型设备故障症状与故障原因之间关系十分复杂，使得传统诊断方法在实际应用中效果不理想。研究采用模糊C-均值聚类算法，将被诊断对象间故障和症状的特征通过建立模糊关系矩阵进行了故障分类，用当前所得的故障征兆群与过去该设备故障征兆结果相对照，找出最相似的结果，从而确定其故障。通过船舶主机轴系诊断的实例，充分证明了该方法的有效性。

Diagnosis Technics of Ship's Fault Bassed on the Fuzzy Clustering Algorithm

The traditional fault detection method for the large equipment was not helpful because of the complicated relationship between the fault symptoms and causes of the equipment. A fuzzy C- means clustering algorithm is used and the features of faults and symptoms of the detected object are classified based on the established fuzzy connection matrix. The comparison between the fault symptom clusters collected from an equipment recently and the previous outcomes of the fault symptoms of that equipment are made, the closest outcomes are identified and the fault is spotted. A case of the recent fault detection for the shafting of main engine fully proves the effectiveness of the above- mentioned method.