CAJ | 학술논문

故障诊断是保障设备安全运行的重要手段。文章采用结构化存储的故障数据模型，考虑到故障数据的特点，以及传统Apriori算法的瓶颈，提出了一种改进的Apriori算法，应用于MapReduce计算框架，减少了算法的扫描次数，提高了算法的执行效率。在云计算环境下对故障数据进行关联规则挖掘，找出发生故障时各设备检测状态的关联关系，为设备维修和管理提供可靠依据。最后给出了该方法的可行性实例验证。
고장진단시보장설비안전운행적중요수단。문장채용결구화존저적고장수거모형，고필도고장수거적특점，이급전통Apriori산법적병경，제출료일충개진적Apriori산법，응용우MapReduce계산광가，감소료산법적소묘차수，제고료산법적집행효솔。재운계산배경하대고장수거진행관련규칙알굴，조출발생고장시각설비검측상태적관련관계，위설비유수화관리제공가고의거。최후급출료해방법적가행성실례험증。
Equipment fault diagnosis is an important means to ensure safe operation of equipment. Structured storage of the fault data model is used in this paper. And an improved Apriori algorithm is proposed accord-ing to the characteristics of fault data and the deficiency of Apriori algorithm, combining the MapReduce programming model, which reduces the number of scanning and improves the efficiency of the algorithm. The correlation is exhumed among equipment inspection status by mining the association rules of fault data in cloud computing environment, which provides reliable support for equipment maintenance and management. At last an example is given to prove the feasibility.