No effective data analysis tool: Limited visibility of equipment conditions / maintenance needs
Large amount of data collected: Rich and valuable data resources, but limited usage in the past
Simple time-based maintenance is employed: Lead to over/lack maintenance and Low efficiency / High safety risks
Central access and comprehensive analysis of all relevant data of an equipment or plant section
Advanced data science technique: machine learning + visual analytics
Flexible and intuitive human-computer-interaction (HCI) to integrate human experience and machine analysis capability
Anomalies prediction allowing "Predictive Maintenance" (condition monitoring) & amp; "Proactive Maintenance" (root cause of risks) for a better life-cycle management of critical equipment
Increased Plant Uptime: Avoid unplanned shut-down of your plant by predicting failures of critical equipment (based on historical data); Analyze behavior of equipment within process environment and find anomalies
Higher Operation Efficiency: Get an intuitive picture of the health of your equipment in a timely and efficient manner; Achieve a predictive maintenance instead of time-based / reactive maintenance; Enable remote monitoring and issue identification
Better Decision Accuracy: Identified correlations hidden in data for smart decisions on operation; Consolidate knowledge, experience and data for 24/7 stable monitoring performance; Reduce workload for limited resources on experienced engineers
