KBR uses predictive analytics and cognitive computing to help you to reduce maintenance expenditures and increase uptime.
Predictive analytics uses historical data, machine learning, computer modelling and statistical analysis to discover patterns and anticipate the future performance and behaviors of a wide variety of complex systems and assets. KBR uses sophisticated predictive analytics combined with our extensive maintenance, engineering and implementation expertise to optimize preventive maintenance schedules to help you to control costs and improve process and asset performance.
“KBR combines data-driven analytics with machine learning modules to help you run your plant closer to optimum targets for longer."
Utilizing first-principle models, real-time performance data and historical maintenance data, we assess your equipment condition and compare it to known metrics for similar systems and operating conditions, providing an accurate real-time evaluation of asset health. The outputs from predictive analytics also quantify the impact of factors contributing to process excursions or asset failures to forecast anomalies, enable early detection and provide prescriptive guidance, reducing unplanned downtime. Our evaluation includes estimated failure probabilities and time to failure for various factors, so your maintenance team can take appropriate remedial action ahead of developing issues.
Additionally, new developments in machine learning and cognitive computing have exciting applications in the area of predictive analytics and asset health. KBR is at the forefront of developing these technologies to further enhance our existing capabilities, augmenting human knowledge with technology. Machine learning uses historical asset data and continuously evolving cognitive models to recommend process improvements and optimize maintenance schedules.