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Our Latest Insights
Articles, webinars, videos and case studies on asset performance management: control valves, pumps and heat exchangers, alarm management, predictive maintenance and the practical use of AI in industrial operations.
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Using Data to Make Maintenance More Predictable
A working project flow for APM: selecting critical assets and non-conformities, choosing failure models and deploying them on live data.
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Using Soft Sensors to Monitor Critical Process Measurements
Soft sensors are AI models that calculate parameters you cannot measure directly, deployable on-premise, embedded or in the cloud.
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Data Sets on the Xcaliber Flow Loop at the Flow Center of Excellence
A time-series data set recorded by UReason and the Flow Center of Excellence from the XCaliber Flow Loop’s Schneider SCADA…
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Risiken von Maschinen und Prozessen in Echtzeit beurteilen
Aus vorhandenen FME(C)As lassen sich Echtzeit-Anwendungen entwickeln. Bow Ties überwachen Maschinen und erkennen Abweichungen rechtzeitig.
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Monitoring Critical Process Measurements Using Soft Sensors
Discover how a soft sensor helps monitor critical process measurements and spot anomalies using real-time data and algorithms.
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Automation Pyramid: Control Level Infographic
Edge computing at the second level of the automation pyramid: how APM Studio analyses asset data at the edge without…
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Edge Computing: Why Should You Care?
Edge computing enables to perform real-time analysis and asset data collection close to the source, making it valuable for both…
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UReason Partners with Ultimo
UReason partnered with Ultimo so its users can add real-time condition monitoring, predictive maintenance and automatic work orders.
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Roadmap to Implementing Predictive Maintenance (PdM): A checklist
Start your smart maintenance journey with our proven predictive maintenance (PdM) best practices. Get the free infographic.
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Business Case Building for Predictive Maintenance Projects
Discover predictive maintenance business case examples built on real-world data and outcomes.
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How to do real-time, data driven, risk assessment on your asset base?
With the Bowtie method you can turn your existing FME(C)A and failure models into real-time cause consequence models that monitor…
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Preventing Asset Outage with Historical Data
What data you need to build real-time condition monitoring, and how to deploy AI and machine learning algorithms on streaming…
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PPA January 2021 with Endress + Hauser
UReason and Endress+Hauser on building a smart plant on the NAMUR Open Architecture concept, with asset degradation forecasting in APM…
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Technohub Initiative
With Krohne Altometer, Valk Welding, Damen Shipyards and others, UReason develops master classes on big data and predictive analytics.
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UReason’s New Logo
Our new logo – a head, a connected brain and a lightbulb – and the slogan ‘Intelligence to Act’, putting…
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Automation Pyramid: Field Level Infographic
APM Studio at the field level of the automation pyramid: reading sensors and control devices to diagnose issues and cut…
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UReason Joins the Institute of Asset Management
UReason is now a corporate member of the Institute of Asset Management, raising awareness of data-driven asset management and its…
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Voorspellend onderhoud: hoe krijg je je organisatie mee?
UReason en MaxGrip laten met asset data zien hoe Aquafin falen kan voorspellen, zodat dringend onderhoud efficiënter wordt ingepland.
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FHI Data Science online meeting
APM Studio unlocks asset data to make predictions, classify degradation and identify energy losses, with AI embedded in your devices.
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Creating dashboards from asset data in minutes
Create Drag & Drop dashboards in minutes that visualise asset information and Key Performance Indicators (KPI) on live data to…
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Article Series: Predictive Maintenance Project Phases
Learn the 5 phases of a predictive maintenance project, from concept to operation, and how to implement PdM effectively.
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Predictive Maintenance (PdM): From theory to practice
How to use data from production processes and combine it with machine learning (ML) and artificial intelligence (AI) models running…
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Using Streaming Data from your Assets to Prevent Outages
Running predictive analytics on streaming data allows you to optimise process availability, detect equipment dysfunctions early and improve asset integrity.
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How to Turn Data from your Process Historian into Value
Where process historians fit in an APM project: selecting critical assets, choosing failure models and deploying predictive maintenance.
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BEMAS | Aquafin Hackathon
UReason and MaxGrip helped Aquafin towards smarter maintenance at the BEMAS hackathon during the Asset Performance 4.0 Conference.
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Ensuring Asset Integrity in the Food and Beverage Industry
Predictive analytics on real-time data helps Food & Beverage companies cut equipment failures and maintenance costs.
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Easy Insights in Failures, Causes and Expected Consequences
Failure, cause and consequence models – Bowtie models – improve maintenance operations, extend remaining useful life and cut downtime.