Authors - S. Zouini, A. Meddaoui, A. Jrifi Abstract - Statistical Process Control (SPC) is a well-established methodology for industrial quality management. The growing complexity of modern manufacturing environments — driven by Industry 4.0, high-dimensional sensor data, and nonlinear process dynamics — exposes the limits of classical monitoring approaches based on fixed thresholds and Gaussian assumptions. This paper proposes an AI-Driven Statistical Process Control (AI-SPC) framework that integrates PCA-based Hotelling’s T2 monitoring with a Random Forest classifier within a closed-loop architecture. The framework is evaluated on five fault scenarios from the Tennessee Eastman Process (TEP) benchmark. Results show that the hybrid AND-logic strategy achieves a false alarm rate of 0.071 — a 45% reduction relative to PCA-T2 alone (0.130) — while maintaining a detection rate of 96.1% and a detection delay of 5.4 samples. A variable contribution analysis further supports fault diagnosis by identifying the most deviant process variables at the moment of detection. These results confirm that combining statistical rigor with data-driven flexibility produces a more reliable and interpretable monitoring system than either approach deployed independently.
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