Robust regression and outlier detection
Robust regression and outlier detection
Scalable robust covariance and correlation estimates for data mining
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Image denoising: a nonlinear robust statistical approach
IEEE Transactions on Signal Processing
Reconstruction of reflectance spectra using robust nonnegative matrix factorization
IEEE Transactions on Signal Processing
Robust condition monitoring for early detection of broken rotor bars in induction motors
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
We introduce a new multivariate statistical process control chart for fault detection using robust statistics and principal component analysis. The proposed approach consists of two main steps. In the first step, a robust covariance matrix is determined using the minimum covariance determinant algorithm. In the second step, an eigen-analysis of the robust correlation matrix is performed to derive the robust control limits of the proposed multivariate chart. Our experimental results illustrate the much better fault detection performance of the proposed method in comparison with existing statistical monitoring and process controlling charts.