## 1 Introduction  
The rotating biological contactor (RBC) is a widely used wastewater treatment system that removes organic pollutants via biofilms attached to rotating disks (see Figure 1). It offers stable performance and is simple to operate and maintain [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. RBCs have also been used for treating landfill leachate, which typically has high levels of biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total nitrogen (T-N) [11, 12, 13, 14]. However, recent shifts in landfill composition, particularly the replacement of combustible waste with incineration ash, have altered leachate characteristics. Incineration ash produces leachate with low organic content and high salinity, which inhibits biofilm formation and reduces treatment performance. A critical issue is calcium scale formation from leachate, which accumulates on disks as calcium carbonate. This can cause mechanical imbalance and, in severe cases, shaft break age [15; 16; 17]. These failures occur suddenly and require costly repairs. In response, RBC designs have evolved to enhance efficiency and durability. Notable innovations include aerofoil-embedded mesh disks [18], nonwoven biofilm carriers [19], membrane-integrated systems [20; 21], and algae-assisted nitrification control [22]. Low-energy approaches such as glycogen-accumulating organisms (GAO)-enriched, non-aerated RBCs and water-wheel-driven systems have also been developed [23]. Meanwhile, AI-based predictive maintenance has gained attention. [PERSON] et al. (2024) proposed an integrated framework for AI-driven fault detection [24], and [PERSON] et al. (2023) emphasized the importance of RBC operational parameters for mechanical stability [25]. These insights underscore the potential of using signals like electric current and noise for AI-based failure prediction. In this study, we aim to prevent such RBC mechanical failures before they occur by analyzing 17-year operational inspection log data routinely recorded at actual leachate treatment facilities. However, this inspection log only includes basic water quality measurements, such as pH and transparency. Concentrations of chemical substances are also factors that affect RBC failure. Since these measurements require analysis costs, they are not included in the inspection log. Thus, this study predicts failure based on mechanical changes, such as abnormal noise and current consumption, which are related to torque. A scoring model was applied to predictive maintenance. This is because prediction errors occur when fitting actual measurements to a model in machine learning and numerical simulation. If failure is underestimated, the model will not be effective for predictive maintenance. Scoring models require the definition of thresholds and weights to generate scores. Traditionally, these have been determined by sensitivity analysis based on specialized knowledge. However, using artificial intelligence (AI) to obtain these values enables landfill operators without professional knowledge to build and adjust models. This research focuses on the practical aspects of predictive maintenance for landfill operators, providing a scoring model they can build and adjust themselves. Note that leachate treatment facilities at landfill sites operate under fundamentally different principles compared to general wastewater treatment facilities. While general treatment facilities are designed to maintain continuous compliance with effluent standards regardless of influent variability, landfill leachate facilities are often permitted to suspend operation once the treated leachate satisfies environmental discharge criteria. Consequently, these systems are often designed with minimal investment, prioritizing basic regulatory compliance over long-term performance or operational resilience. Furthermore, to reduce operating costs, routine inspections and maintenance are often limited, which increases the risk of undetected equipment degradation or failure. Figure 1: Schematic overview of a rotating biological contactor (RBC) system.