| Title | Multidimensional Reconstruction-Based Contribution for Multiple Faults Isolation with k-Nearest Neighbor Strategy |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Yuanle, Xu, Chengjie, Wang, Yanwei, Yang, Weidong, Zheng, Ying |
| Conference Name | 2021 40th Chinese Control Conference (CCC) |
| Keywords | Benchmark testing, Contribution based on k nearest neighbors, Correlation, Cyber-physical systems, fault diagnosis, human factors, Input variables, Metrics, Multi-dimensional reconstruction based contribution, multiple fault diagnosis, PCA, Pollution, principal component analysis, process control, pubcrawl, Resiliency |
| Abstract | In the multivariable fault diagnosis of industrial process, due to the existence of correlation between variables, the result of fault diagnosis will inevitably appear "smearing" effect. Although the fault diagnosis method based on the contribution of multi-dimensional reconstruction is helpful when multiple faults occur. But in order to correctly isolate all the fault variables, this method will become very inefficient due to the combination of variables. In this paper, a fault diagnosis method based on kNN and MRBC is proposed to fundamentally avoid the corresponding influence of "smearing", and a fast variable selection strategy is designed to accelerate the process of fault isolation. Finally, simulation study on a benchmark process verifies the effectiveness of the method, in comparison with the traditional method represented by FDA-based method. |
| DOI | 10.23919/CCC52363.2021.9550385 |
| Citation Key | liu_multidimensional_2021 |
Multidimensional Reconstruction-Based Contribution for Multiple Faults Isolation with k-Nearest Neighbor Strategy