For the multimode process, the scale information of every single mode never be considered in the distance calculation between the data and its neighbors in k nearest neighbor (kNN). This work proposes a standardized kNN (SkNN) based fault detection method, where a standardized distance is developed to characterize the distance between the data and its neighbors taking the scale information within mode and mode to mode into consideration. In addition, compared with the kNN based fault diagnosis method, the importance of various neighbors is considered through constructing the weights and giving to different neighbors in the SkNN based fault diagnosis method. Moreover, when there is more than one fault variable, in order to eliminate the influence of other fault variables on current reconstructed variable and reduce the computational complexity, concurrent reconstructed strategy and greedy algorithm are used in the SkNN based fault diagnosis method. At last, an industrial case study is employed to prove the effectiveness and advantage of the proposed SkNN based fault detection and diagnosis method.

对于多模式过程，在数据与k个最近邻居（kNN）中的邻居之间的距离计算中，永远不会考虑每个单一模式的比例信息。这项工作提出了一种基于标准kNN（SkNN）的故障检测方法，其中考虑了模式和模式间的比例信息，开发了标准距离来表征数据与其邻居之间的距离。另外，与基于kNN的故障诊断方法相比，在基于SkNN的故障诊断方法中，通过构造权重并赋予不同的邻居，考虑了各个邻居的重要性。此外，当存在多个故障变量时，为了消除其他故障变量对当前重构变量的影响并降低计算复杂度，基于SkNN的故障诊断方法中采用了并行重构策略和贪婪算法。最后，通过工业案例研究证明了所提出的基于SkNN的故障检测和诊断方法的有效性和优势。