Representative End User Clients
Representative Automation Clients
Representative Software Clients
ARC has been blogging (and reporting on) a lot these days on how Industrial Internet of Things (IIoT) technologies such as smart sensors, predictive analytics, and machine learning can be applied to improve the availability, reliability, and performance of industrial assets and enable new business models. ARC believes end users can enjoy a step change in maintenance and reliability effectiveness by adopting recently developed advanced machine learning platforms. Integrating machine learning techniques and cognitive analytics provide end users advance warning of impending equipment failure, enabling companies adequate time to remediate the problem prior to failure to avoid costly unplanned downtime.
Historically, pump monitoring involved measuring predetermined thresholds of vibration and temperature and sending alerts whenever these measurements exceeded a threshold. The key limitation to this approach is that it often does not provide sufficient advance notice of impending failures, as threshold alarms provide notification only hours before anticipated failure. Another key limitation to this approach is the time required to develop and maintain the necessary algorithms. Making matters worse, threshold monitoring techniques typically do not adequately detect unknown operating states with variable process conditions.
Recent advances in data science, artificial intelligence, and computing power have converged to enable commercialization of algorithms to provide human-like analysis of large data sets (structured or unstructured) at machine speeds. Cognitive analytics refers to a set of innovations inspired by the way the human brain thinks. It strives to provide machines with the ability to process complex unstructured data sets by continuously learning and drawing conclusions from new data entering the system as well as from previous insight. Ultimately, cognitive analytics breaks away from traditional rules-based models to new models relying on hypothesis generation based on multiple data sets that codifies human instincts and experience into machine learning.
Flowserve recently presented how Flowserve turned to external technology partner, SparkCognition, to develop a predictive model to predict future pump failures within thirty days. SparkCognition, a cognitive analytics company, applies machine learning and artificial intelligence to cloud security and the Internet of Things. This partnership enabled Flowserve to co-develop automated equipment failure prediction capabilities without the typical lengthy development cycle. Through its partnership with SparkCognition, Flowserve has finally been able to effectively answer questions such as: Is the pump operating in the right state? Is there a problem with the pump? What kind of problem is the pump experiencing? Is it a problem they’ve never seen before? The company can now create dynamic models of pumps under various operating conditions and apply multi-dimensional analysis to provide early warning of potential equipment failures, often well before they could be detected by humans, if at all.
To learn more, check out an exciting and informative webinar AI/Machine Learning - Why Oil & Gas Needs This on June 1st in which ARC will be joined by from representatives from Flowserve and SparkCognition to delve deeper into this exciting topic.
Historically, pump monitoring involved measuring predetermined thresholds of vibration and temperature and sending alerts whenever these measurements exceeded a threshold. The key limitation to this approach is that it often does not provide sufficient advance notice of impending failures, as threshold alarms provide notification only hours before anticipated failure. Another key limitation to this approach is the time required to develop and maintain the necessary algorithms. Making matters worse, threshold monitoring techniques typically do not adequately detect unknown operating states with variable process conditions.
Recent advances in data science, artificial intelligence, and computing power have converged to enable commercialization of algorithms to provide human-like analysis of large data sets (structured or unstructured) at machine speeds. Cognitive analytics refers to a set of innovations inspired by the way the human brain thinks. It strives to provide machines with the ability to process complex unstructured data sets by continuously learning and drawing conclusions from new data entering the system as well as from previous insight. Ultimately, cognitive analytics breaks away from traditional rules-based models to new models relying on hypothesis generation based on multiple data sets that codifies human instincts and experience into machine learning.
Flowserve recently presented how Flowserve turned to external technology partner, SparkCognition, to develop a predictive model to predict future pump failures within thirty days. SparkCognition, a cognitive analytics company, applies machine learning and artificial intelligence to cloud security and the Internet of Things. This partnership enabled Flowserve to co-develop automated equipment failure prediction capabilities without the typical lengthy development cycle. Through its partnership with SparkCognition, Flowserve has finally been able to effectively answer questions such as: Is the pump operating in the right state? Is there a problem with the pump? What kind of problem is the pump experiencing? Is it a problem they’ve never seen before? The company can now create dynamic models of pumps under various operating conditions and apply multi-dimensional analysis to provide early warning of potential equipment failures, often well before they could be detected by humans, if at all.
To learn more, check out an exciting and informative webinar AI/Machine Learning - Why Oil & Gas Needs This on June 1st in which ARC will be joined by from representatives from Flowserve and SparkCognition to delve deeper into this exciting topic.