Reference number : GC-2017-Q4-01086, amendment number : , agreement type : G, recipient legal name : Liu, Jinfeng (University of Alberta), recipient country : CA, recipient province : nan, recipient city : Alberta, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : Abnormal situations cost industry millions of dollars every year. Alarm systems play important roles in_x000D_
ensuring safe and efficient operation of modern industrial plants. However, most existing industrial alarm_x000D_
systems suffer from alarm overloading. This implies that operators have to deal with too many alarms that_x000D_
exceed the operators' capacity. Typically, plant operators receive much more alarms than they can handle._x000D_
There has been an increasing interest in industry to address this issue and seek remedies to reduce the number_x000D_
of false and nuisance alarms. Moreover, the current alarm messages do not indicate the root causes of the_x000D_
alarms or how to solve the alarms. An effective root cause analysis is highly desired._x000D_
NTWIST Inc. provides advanced real-time alarm management solutions to process industries. Their goal is to_x000D_
eliminate preventable valve closures, unplanned equipment shutdowns and unplanned facility_x000D_
outages/shutdowns caused by alarm flooding and alarm suppression. Currently, NTWIST is developing a_x000D_
real-time alarm management system based on artificial intelligence. In this project, NTWIST works together_x000D_
with Dr. Liu's group in the University of Alberta to learn from historical alarm data using reinforcement_x000D_
learning and deep learning to address the issues with alarm flooding based on each alarm's impact on the_x000D_
process and safety conditions., program purpose french : Abnormal situations cost industry millions of dollars every year. Alarm systems play important roles in_x000D_
ensuring safe and efficient operation of modern industrial plants. However, most existing industrial alarm_x000D_
systems suffer from alarm overloading. This implies that operators have to deal with too many alarms that_x000D_
exceed the operators' capacity. Typically, plant operators receive much more alarms than they can handle._x000D_
There has been an increasing interest in industry to address this issue and seek remedies to reduce the number_x000D_
of false and nuisance alarms. Moreover, the current alarm messages do not indicate the root causes of the_x000D_
alarms or how to solve the alarms. An effective root cause analysis is highly desired._x000D_
NTWIST Inc. provides advanced real-time alarm management solutions to process industries. Their goal is to_x000D_
eliminate preventable valve closures, unplanned equipment shutdowns and unplanned facility_x000D_
outages/shutdowns caused by alarm flooding and alarm suppression. Currently, NTWIST is developing a_x000D_
real-time alarm management system based on artificial intelligence. In this project, NTWIST works together_x000D_
with Dr. Liu's group in the University of Alberta to learn from historical alarm data using reinforcement_x000D_
learning and deep learning to address the issues with alarm flooding based on each alarm's impact on the_x000D_
process and safety conditions., agreement title english : Industrial Alarm Management using Reinforcement Learning, agreement title french : Industrial Alarm Management using Reinforcement Learning, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-01-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada