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Maintenance downtime tracking<\/strong> is the application of downtime measurement to maintenance management \u2014 using the precise record of every equipment stoppage to calculate MTBF and MTTR, detect equipment degradation before it causes failure, optimise preventive maintenance schedules, and measure the effectiveness of maintenance interventions. While production downtime tracking focuses on availability improvement from an operational perspective, maintenance downtime tracking focuses on the same data from a reliability engineering perspective: how do we make equipment fail less often and recover faster when it does?<\/p>\n This guide covers the complete maintenance downtime tracking framework: the metrics that matter, how to use downtime data to transition from reactive to predictive maintenance, and how JEMBA AI in TeepTrak automates the analysis that would otherwise require a dedicated reliability engineer.<\/p>\n 1. Reactive maintenance (run to failure):<\/strong> no planned intervention \u2014 wait for the breakdown, then repair. Downtime tracking in this context provides the historical breakdown record needed to identify which machines fail most often and which failures cost the most production time. This is the starting point for any maintenance improvement programme \u2014 without knowing which machines break most often, there is no basis for prioritising preventive maintenance investment.<\/p>\n 2. Time-based preventive maintenance (PM):<\/strong> scheduled maintenance at fixed intervals \u2014 every N hours of operation or every calendar period. Downtime tracking provides the actual running hours data (from IoT sensor runtime monitoring) needed to implement true usage-based PM rather than calendar-based PM. A machine that runs 3,000 hours\/year should receive its 500-hour service after 500 hours of actual operation \u2014 not after a fixed calendar period that may correspond to 350 or 650 actual running hours depending on production scheduling.<\/p>\n 3. Condition-based maintenance:<\/strong> maintenance triggered by measured condition parameters \u2014 temperature, vibration, current consumption, cycle time deviation. TeepTrak’s IoT sensors provide continuous current consumption monitoring that detects the electrical signature changes associated with bearing wear, motor degradation and mechanical fatigue \u2014 without additional vibration or temperature sensors. For a comprehensive overview of condition monitoring tools, see Reliabilityweb.com<\/a>, the industry reference for reliability engineering practices.<\/p>\n 4. Predictive maintenance (PdM):<\/strong> JEMBA AI analyses the continuous stream of IoT downtime data to detect the degradation signatures that precede equipment failure \u2014 progressive micro-stoppage frequency increases, cycle time drift, current consumption anomalies \u2014 and generates maintenance alerts 24 to 72 hours before the predicted failure. This is the highest-maturity maintenance strategy, and it is now accessible without a dedicated reliability engineering team. MESA International’s Manufacturing Operations Management guidelines<\/a> position predictive maintenance as the benchmark for world-class manufacturing maintenance practice.<\/p>\n The core maintenance downtime metrics calculated automatically from TeepTrak downtime tracking data:<\/p>\nThe Four Maintenance Strategies and How Downtime Tracking Enables Each<\/h2>\n
Maintenance Downtime Tracking Metrics: MTBF, MTTR and Beyond<\/h2>\n