commit 3c25357429ab51e81bffa357d1c7f94f97547632 Author: kellyestahl06 Date: Thu Apr 16 16:56:03 2026 +0800 Add Forge Reliability: Building a Predictive Maintenance Program That Reduces Downtime diff --git a/Forge-Reliability%3A-Building-a-Predictive-Maintenance-Program-That-Reduces-Downtime.md b/Forge-Reliability%3A-Building-a-Predictive-Maintenance-Program-That-Reduces-Downtime.md new file mode 100644 index 0000000..a83352a --- /dev/null +++ b/Forge-Reliability%3A-Building-a-Predictive-Maintenance-Program-That-Reduces-Downtime.md @@ -0,0 +1,2 @@ +
Unplanned downtime often comes from repeating equipment issues that are not detected early or not addressed with a consistent strategy. A strong PdM program usually starts by identifying critical equipment, building inspection routes and establishing baselines for condition monitoring measurements. Vibration analysis can detect imbalance, misalignment and bearing wear, while infrared thermography can reveal electrical and mechanical hot spots. Ultrasound inspections can help identify compressed air leaks and early bearing defects. Beyond inspections, maintenance strategy consulting helps align tasks with failure modes and production needs. Root cause failure analysis can also reduce repeat breakdowns by addressing underlying drivers rather than symptoms. [Forge Reliability](https://forge-reliability-predictive-maintenance.donna-patel.workers.dev/) focuses on practical program setup and coaching so teams can collect useful data, interpret results and convert findings into planned work. With consistent routes, clear thresholds and a maintenance strategy tied to asset criticality, organizations can reduce downtime and improve reliability outcomes over time. +
\ No newline at end of file