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Journal Article
Robust Mahalanobis distance-based lazy learning method for fault detection in high-dimensional processes
- Authors
- Jungwon Yu, Kwang-Ju Kim, In-Su Jang
- Issue Date
- 2025-09
- Citation
- ETRI Journal, v.권호미정, pp.1-16
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- When using lazy learners based on the Mahalanobis distance (MD) function for process fault detection (FD), due to the curse of dimensionality, type I errors can increase significantly as the number of process variables increases. In high-dimensional data spaces, certain regions exist in which data samples are sparsely distributed. From the perspective of dense regions, the outlierness (i.e., degree of being statistical outliers) of samples in sparse regions increases as the data dimensions increase, leading to unstable estimations of classical covariance matrices for calculating MD function values. To solve this problem, a lazy learning method is proposed based on a robust MD function, where robust covariance matrices are estimated using a minimum covariance determinant method. Here, k-nearest neighbors and local outlier factor are employed as baseline learners. The proposed method can be applied to all types of lazy learning techniques. To verify FD performance, the proposed method is applied to two benchmark processes. The experimental results show that the proposed method can perform FD on very high-dimensional processes successfully without rapid increases in type I errors.
- KSP Keywords
- Covariance matrix, Data dimensions, Data samples, Distance-based, High-dimensional data, K-Nearest Neighbor(KNN), Lazy learners, Learning methods, Local outlier factor(LOF), Minimum covariance determinant, Process variables
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
