Edge Analytics and the Industrial Internet of Things
Learn more about how you can overcome the challenges you might face doing edge computing when your equipment is in remote or even moving locations.
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The Reality of Predictive Equipment Maintenance: Cluster-Based Anomaly Detection For Industrial Operations
Many heavy industrial companies are seeking to implement predictive analytics to improve revenue, reduce costs, and improve safety across their operations. However, it is almost impossible to implement true predictive maintenance systems without significant historical operating data and a large number of repeated failures.
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Smart Services: Data-Driven Consulting and the Industrial Internet of Things
Leading heavy industrial companies are now taking advantage of the emerging data landscape to increase revenue, decrease costs, and creating new business models.
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Smart Iron: Industrial OEMs and the Internet of Things
OEMs supplying heavy industries must innovate and adapt to the possibilities of the Industrial Internet of Things.
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The Executive Guide to Industrial Internet of Things
In this whitepaper you will learn more about the industrial internet of things (IIoT) and how it will disrupt traditional thinking.
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Arundo Edge Agent
Arundo Edge Agent enables industrial connectivity and analytics in rugged, remote, or disconnected environments. Watch our animation here.
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ABB Arundo Virtual Flow Meter
Find out how ABB and Arundo's Virtual Flow Meter software can help oil and gas operators maximise hydrocarbon production, reduce CAPEX and OPEX and do so on hundreds of wells.
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Arundo Maritime Suite powered by Veracity
Find out how Arundo and Veracity enable crews, fleet operations, and maintenance personnel, on ship and on shore, to easily select, stream, compute and run advanced analytics. Arundo Maritime Suite brings advanced analytics and machine learning to ship and shore, allowing mariners to easily connect, visualize, and analyze critical process data.
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2 - Typical process for developing predictive equipment models
In this video, Alexandra discusses a typical process for data scientists building predictive equipment models.
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4 - Building and training automated topic labelling models
In this video, Alexandra shows how to automatically classify massive amounts of historical failure data.
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5 - Building and training automated sensor labelling models
In this video, Alexandra shows you how to automatically label sensors based on piping & instrumentation diagrams.
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1 - Data requirements for industrial machine learning
In this video, Alexandra discusses the four major types of data required to develop predictive models for heavy industrial equipment.
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3 - Organizing data for industrial data science
In this video, Alexandra shares the methodology behind the tools we are building to structure and link data from different sources within asset-heavy industries.
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Data-Driven Hydrocarbon Production Optimization: Opportunities and Challenges
Learn how hybrid physical and statistical models can work to offer new and improved insights to upstream oil and gas production.
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Equipment performance: Unlock the digital potential of your current data through advanced analytics
Find out how to maximize the impact of your data through analytics and contextualization. Be able to see operation history for a single piece of equipment, assess equipment performance, identify equipment that breaks down, benchmark equipment utilization, benchmark equipment energy consumption.
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Digital Ships: Connected Vessels and Machine Learning Applications for Maritime Operations
Data acquisitions, storage, processing,and communication continue to get cheaper. Learn how leading companies are transforming their business through IoT and machine learning applications.
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End-to-End Pump System Analytics
In this webinar you will learn how you can get started with advanced analytics in your equipment.
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Big Data Analytics to Optimize Water Consumption
Learn how we safely reduced a ships' on-vessel water quantity by 15%, leading to significantly decreased fuel usage and increased cargo space.
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Cloud-Based Machine Learning Models to Determine Potential Misconnection and Shock Hazard on Smart Meter Installations
Determining the quality of smart meter installations are often difficult without physical site presence and local electrical testing. Learn how we solved it for a European power utility.
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Benchmarking Asset Performance
An international oil and gas company had trouble with their asset performance and equipment failure rates. Learn how our solution reduced non-productive drilling time.
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Offshore Logistics: Forecasting Product to Predict and Optimize Repair Routes
A large national oil company had reactive repairs and supply chain decisions resulting in high rig non-productive time. Learn how we used forecasting product to predict and optimize repair routes.
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Condition-Based Monitoring Platform for Heat Exchangers
A leading oil and gas company wanted to reduce hazardous leakage risk on their heat exchangers. Learn how we used condition-based monitoring to detect subtle precursors to failure.
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Condition-Based Monitoring: Gas Compressors
A large oil company sought to reduce yearly equipment maintenance cost by 10%. Learn how we solved it using a condition-based monitoring solution for gas compressors.
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