Fault detection and diagnosis via standardized k nearest neighbor for multimode process

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. (C) 2019 Published by Elsevier B.V. on behalf of Taiwan Institute of Chemical Engineers.