Being able to monitor and check for equipments and machines health in near real-time and trigger an alert to notify responsible operator if certain conditions threshold are detected.
We collect and integrate maintenance-related data from multiple sources like inspection data (oil sampling, visual inspection report, etc.), sensors data (vibration, pressure, temperature, voltage, RPM), and maintenance post-event data (damage classes, maintenance types, planned v.s. unplanned events, maintenance actions, and maintenance subcomponents. Then with the help of machine learning model, we combine the information from OEM recommendation for maintenance condition with all the historical data and Subject Matter Expert experience to provide the most accurate and reliable prediction of the assets health condition in near real-time.