Decision making methodology for maintenance of return pumps in wastewater treatment plant using machine learning

The current maintenance systems for pumps in wastewater treatment plants operate reactively, focusing on simple reinforcement measures when faults are detected rather than applying mid- to long-term strategic management. A maintenance paradigm that accurately predicts the condition of machines offers the potential to reduce maintenance costs and improve facility operations through preemptive strategies. This study emphasizes decision-making for maintenance management by establishing a real-world monitoring system, extracting operational data, and going through processes, namely, determination, collection, analysis, and modeling. Thresholds for asset conditions are set, and indicators are established for assessment and grading. Based on this, in predictive maintenance, extracted data are used to measure parameters through machine learning techniques and to prediction future performance for condition monitoring. This study presents a methodology for proactive pump asset management and suggests key considerations in maintenance strategies. The study strongly suggests that preventive pump maintenance can significantly reduce the risk of prolonged wastewater treatment outages and environmental discharges.