KBR uses predictive analytics and cognitive computing to help you reduce maintenance expenditures and increase uptime.
Predictive analytics uses historical data, computer modelling and statistical analysis to discover patterns and anticipate the future performance of a wide variety of complex systems. KBR uses sophisticated predictive analytics combined with our extensive maintenance, engineering and execution expertise to optimize preventive maintenance schedules to help you control costs and improve performance.
"KBR uses sophisticated predictive analytics to help you control costs and improve performance."
Utilizing first-principle models, real-time performance data and maintenance historical data, we assess your equipment condition and compare it to known metrics for similar systems and operating conditions, fine-tuning your maintenance schedules to promote savings and reduce 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 asset historical data and continuously evolving cognitive models to recommend process improvements and optimize maintenance schedules.