在两维空间中,当关键质量特性之间存在相关关系并且预定义故障类之间重叠时,传统的模糊聚类算法FCM对双故障并发的识别率会下降.为了提升对重叠并发双故障的识别率,一种新算法PILDA被提出,该算法提出的主成分修整能够消除重叠的影响,而双故障判别区间确定的方法则能够实现对未预定义的并发双故障的识别.经过864种不同相关关系和均值偏移量的故障组合仿真实验,结果表明PILDA能有效识别并发故障及预定义单发故障,平均识别率为84.94％,明显高于FCM的58.13％.该方法具有一定的应用价值.%In the two-dimension space, when there is a correlation between two critical-to-quality (CTQ) attributes, two predefined faults overlap each other. In this case, if fuzzy c-means (FCM) method is applied to detect these two faults that happen simultaneously, the detection rate is low. In order to solve this problem, a new algorithm called linear discriminant analysis (LDA) with principal component analysis ( PCA) and discriminant intervals ( PILDA) is proposed. By this algorithm, PCA-shaping is used to eliminate the effects of overlapping and to determine the discriminant intervals so as to overcome the difficulty in detecting two faults that are not predefined. To verify the proposed method, simulation is made for the detection of simultaneous Faults and single faults by using 864 fault combinations in different variable relations and different variable shifts. By the proposed method, the average detection rate is 84. 94% compared with 58. 13% by FCM. This is a significant improvement.
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