Evaluation of Condition Monitoring and Predictive Maintenance Implementation in a Sugar Processing Plant in Jigawa State

Sugar processing facilities in northern Nigeria operate capital-intensive rotating and reciprocating machinery including mills, centrifuges, turbines, and crystalliser drives under severe process loading conditions that make equipment reliability a determinant of seasonal production outcomes. This study evaluates the implementation status and effectiveness of condition monitoring and predictive maintenance practices at a sugar processing plant in Kazaure, Jigawa State. The assessment was conducted through structured interviews with maintenance engineers and technicians, review of condition monitoring data and maintenance records, and on-site inspection of monitoring instrument deployments. Condition monitoring maturity was scored against a five-level model covering vibration analysis, infrared thermography, oil analysis, and ultrasonic inspection capabilities. Results indicated that the plant had achieved Level 2 condition monitoring maturity, with regular vibration monitoring in place for major rotating machines but limited systematic use of diagnostic findings to schedule predictive interventions. Oil analysis was conducted intermittently and thermographic surveys were performed annually rather than on a risk-based frequency. False alarm rates in vibration monitoring were estimated at 28 percent, reducing technician confidence in monitoring outputs. Correlation analysis of condition monitoring alerts and subsequent confirmed failures showed a predictive accuracy of 67 percent for the vibration monitoring programme. The study recommends progression to a Level 3 maturity framework through analyst training, reduction of false alarm rates, and integration of condition data into the computerised maintenance management system. Keywords: condition monitoring, predictive maintenance, sugar processing, vibration analysis, Jigawa State.