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中文摘要：
在故障诊断中,从包含冗余和不一致信息的数据中获取简单有效的诊断决策规则是一个难题。首先,针对完备信息系统和不完备信息系统分别提出了相应的融合算法,为解决数据超载以及不完整信息融合问题提供了有效的方法。其次,提出了基于粗糙集理论的多源信息融合故障诊断模型。该模型从包含冗余和不一致信息的原始数据出发,利用基于改进属性重要度的方法实现故障征兆属性约简;然后通过给出的值约简算法进一步产生最大广义决策规则集,建立了用于故障诊断的规则库。最后,在应用该模型进行故障诊断时,用待诊断实例的离散化了的故障征兆属性与规则库中的诊断决策规则进行匹配,对返回的诊断决策规则依据置信度、覆盖度和支持度进行综合评价,并得出诊断结论。给出的诊断实例验证了该方法的可行性和有效性。
英文摘要：
Extraction of simple and effective decision rules from the numerous data containing inconsistent and redundant in formation is one of the most important issues needed to be solved in fault diagnosis. Firstly, According to complete information system and incomplete information system, the corresponding fusion algorithms are shown, which provide an effective method to deal with the overloading data of sensors and informa tion fusion for incomplete sensors. Secondly, a multi-sensor information fusion fault diagnosis model based on rough set theory is presented. From original fault data containing inconsistent and redundant information, the fault symptom attributes are reduced using the method based on attribute significance. Then, a set of maximal generalized decision rules are generated by using a proposed value reduction algorithm, and a decision rule base for fault diagnosis is established. Finally, when the presented model is applied for fault diagnosis, the dis cretized fault symptom attributes are matched with the rules in the decision rule base, and the returned diagnostic decision rules are evaluated by using certainty factor, coverage factor and support factor, then diagnostic results are obtained. A proposed diagnostic example proves the method is feasible and available.
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