Data-driven fault detection and diagnosis methods in wastewater treatment systems: A comprehensive review

Wastewater treatment systems are essential for sustainable water resource management but face challenges such as equipment and sensor malfunctions, fluctuating influent conditions, and operational disturbances that compromise process stability and detection accuracy. To address these challenges, this paper systematically reviews data-driven fault detection and diagnosis (FDD) methods applied in wastewater treatment systems from 2014 to 2024, focusing on their applications, advancements, and limitations. Main contributions include an overview of key treatment processes, a detailed evaluation of fault types (process and sensor faults), advancements in multivariate statistical methods, machine learning (ML), and hybrid FDD techniques, as well as their effectiveness in anomaly detection, managing complex data distributions, and enabling real-time monitoring. Furthermore, the paper highlights critical challenges such as data quality and model interpretability, proposing actionable future directions, including the development of explainable artificial intelligence, adaptive real-time processing, and cross-system generalizability. These insights are intended to guide the development of robust, scalable, and interpretable FDD solutions, ultimately improving the efficiency, reliability, and sustainability of wastewater treatment systems.