Data-Driven Fault Detection for HVAC Control Systems in Pharmaceutical Manufacturing Workshops

Large-scale heating, ventilation, and air conditioning (HVAC) control systems in pharmaceutical manufacturing are characterized by complex operational parameters, delayed and often challenging fault detection, and stringent regulatory compliance requirements. To address these issues, this study presents an innovative data-driven fault detection framework that integrates Principal Component Analysis (PCA) with Nonlinear State Estimation Technology (NSET), specifically tailored for highly regulated pharmaceutical production environments. A dataset comprising 13,198 operational records was collected from the SCADA system of a pharmaceutical facility in Zhejiang, China. The data underwent preprocessing and key parameter extraction, after which a nonlinear state estimation predictive model was constructed, with PCA applied for dimensionality reduction and sensitivity enhancement. Fault detection was performed by monitoring deviations in the mixing room temperature, identifying faults when the residuals between observed and predicted values exceeded a statistically determined threshold (mean ± three standard deviations), in accordance with the Laida criterion. The framework’s effectiveness was validated through comparative analysis before and after documented fault events, including temperature sensor drift and abnormal equipment operation. Experimental results demonstrate that the proposed PCA-NSET model enables timely and accurate detection of both gradual and abrupt faults, facilitating early intervention and reducing potential production downtime. Notably, this framework outperforms traditional fault detection methods by providing higher sensitivity and specificity, while also supporting continuous quality assurance and regulatory compliance in pharmaceutical HVAC applications. The findings underscore the practical value and novelty of the integrated PCA-NSET approach for robust, real-time fault detection in mission-critical industrial environments.