基于模糊c-均值-粗糙集-自适应模糊神经网络推理系统集成的故障诊断

考虑模糊聚类的数据离散功能，粗糙集理论对决策系统的约简能力，以及模糊神经网络在模式识别方面具有的优势，提出了模糊c-均值(FCM)—粗糙集—白适应模糊神经网络推理系统(ANFIS)集成进行故障诊断的方案：首先，应用FCM聚类方法离散故障诊断数据中的连续属性值；然后，基于粗糙集理论计算诊断决策系统的约简，按照实际需要确定诊断条件；最后，根据系统约简设计ANFIS进行故障诊断。4135柴油机的实际诊断结果验证了文中提出集成故障诊断方案的可行性。在数据充分的条件下，该方案可以推广应用于其它机械设备。

Fault Diagnosis Based on Integration of Fuzzy c-Means, Rough Sets and Adaptive Neuro-Fuzzy Inference System

Considering the ability of fuzzy clustering to discretization of data, that of rough sets theory to reduction of decision system, and that of fuzzy neural networks to nonlinear mapping, a new hybrid system of fuzzy c-means (FCM), rough sets theory, and adaptive neuro-fuzzy inference system (ANFIS) for intelligent fault diagnosis was presented. Firstly, the continuous attributes in diagnostic decision system were discretized with FCM. Then, reducts were found based on rough sets theory, and the key conditions for diagnosis were determined. Lastly, according to the chosen reduct, the ANFIS was designed for fault diagnosis. The diagnosis of a diesel demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis, and verified the feasibility of engineering application. With enough samples, the solution can be applied to other machinery.