Louifi, AbdelhalimKouadri, AbdelmalekHarkat, Mohamed-FaouziBensmail, AbderazakMansouri, Majdi2026-01-252025https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11030568https://dspace.univ-boumerdes.dz/handle/123456789/16005Fault detection in industrial processes is challenging due to significant data uncertainty, which complicates the accurate modeling of interval-valued data and the quantification of errors necessary for reliable detection. Existing approaches, such as kernel principal component analysis (KPCA), struggle with these challenges because they rely on single-valued data representations and are unable to effectively handle interval-based variability. To address these limitations, this paper introduces the interval-valued model KPCA (IV-KPCA), which extends KPCA by redefining similarity measures and kernel functions to accommodate interval-valued uncertainty. IV-KPCA preserves the interval structure throughout the modeling process, enhancing robustness to dynamic uncertainties and improving fault detection in complex nonlinear systems. Within this framework, fault detection statistics (T 2 , Q, and 8) are developed to enable precise error quantification. The proposed method is validated on a cement rotary kiln process, a highly stochastic industrial system characterized by significant uncertainties. Experimental results demonstrate that IV-KPCA reduces false alarms, missed detections, and detection delays by over 100%, 90%, and 95%, respectively, compared to traditional methods. These findings underscore the potential of IV-KPCA in enhancing fault detection performance in complex, uncertain environmentsenFault detectionKernel principal component analysis (KPCA)Interval-valued kernel PCA (IV-KPCA)Cement rotary kilnArticle