中文摘要：

针对传统聚类方法需预先指定类别个数而导致应用受限的问题,提出一种基于ART和Yu范数的聚类方法,可自适应地确定类别个数.通过对齿轮无标记故障样本的诊断分析对该方法进行验证.从多个角度提取反映故障信息的特征参数集,利用距离区分技术对其进行优选,并结合ART的机制和基于Yu范数的聚类技术,对齿轮故障类别进行诊断分析,并与FuzzyART方法的诊断结果进行比较.结果表明,该方法可以有效地对齿轮故障进行区分,且效果优于FuzzyART方法.

英文摘要：

As the traditional clustering method needs to determine the number of classes in advance,a novel clustering method based on adaptive resonance theory (ART)and Yu norm that can self-adapt to determine the number of classes is proposed and validated by the diagnostic analysis of unlabeled faulty samples of gears.A feature parameter set that presents the fault-related information is extrac-ted from different symptom domains,and some optimal features are selected by the distance discrimi-nant technique.Having combined the merits of ART and Yu norm-based clustering method,the pro-posed clustering model is employed to diagnose the fault conditions of gears and found to be able to ef-fectively classify the faulty samples of gears,having better diagnosis performance than the fuzzy ART.