by Gary Mintchell | Jan 23, 2026 | Asset Performance Management, Generative AI
I seldom hear from Emerson lately. They have announced an update to the AspenTech Mtell asset performance management portfolio. They mention AI-enabled functions. The company has had aspects of artificial intelligence buried within the system for years. This expands its usefulness.
Emerson announced the next evolution of its AspenTech Asset Performance Management (APM) portfolio. The latest release of Aspen Mtell provides a pathway for companies to drive immediate value and seamlessly scale from foundational asset health monitoring to best-in-class, AI-enabled failure prediction and continuous operational improvement.
The latest innovations in Aspen Mtell enable a proactive enterprise reliability program that delivers continuous improvement. Key capabilities and updates to Aspen Mtell include:
- Rapid Scalability: Industry- and asset-specific templates and market-leading analytics drive faster deployment of asset health monitoring across the enterprise, enabling quick ROI and seamless transition to AI-driven prediction.
- Accelerated Alert Resolution: AI-powered insights automatically group and prioritize alerts based on severity, risk and historical data. Embedded failure mode and effects analysis prescribes corrective action, significantly streamlining risk resolution.
- Next-Level Operational Reliability: Direct connection with Emerson’s vibration monitoring solutions, AMS Machine Works and AMS Device Manager.
- Seamless Enterprise Integration: Deliver actionable insights directly into existing enterprise resource planning workflows through deep integration with enterprise asset management systems.
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by Gary Mintchell | Oct 29, 2025 | Asset Performance Management, Data Management, Education, News
I find it sort of amazing that the head of product development at the manufacturing company where I was working in a position sort of like a combination of manufacturing engineering and materials management plucked me out of the factory to assume a role a manager of data.
That was 1976. The problems I attempted to solve 50 years ago are the same problems (albeit on a much larger scale) that executives face today. Multiple silos of proprietary data. Insufficient insight into the company’s operational health. Buried risks to enterprise decision-making.
Next week (Nov. 5-6), Texas A&M Department of Construction Science ADIF Working Group hosts its 2nd ADIF workshop.
ADIF (Asset Data Interoperability Framework) working group is a dedicated research group of industry experts and academia that is committed to fostering open, vendor-neutral, and standards-based solutions for achieving data and systems interoperability for assets intensive industries.
I will be in College Station next week to moderate a panel discussion on standards—perhaps discussing how so many standards can work together. The panel includes luminaries Markus Stumptner, University South Australia, Alan Johnston, MIMOSA, Micheal Wiedau and Reiner Meyer-Rossl, DEXPI, Peter Townson, CHIFOS, and Chris Monchinski, ISA 95.
There is still time to register and come. I will probably have some live reports for those who cannot make it.
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by Gary Mintchell | Oct 28, 2025 | Asset Performance Management, Data Management, Manufacturing IT, Software
AVEVA updated its software offering by converging all data into its Connect platform.
AVEVA is converging all data onto CONNECT industrial intelligence platform. Through enhancements to AVEVA Asset Information Management, AVEVA System Platform and AVEVA PI Data Infrastructure, AVEVA can enable the visualisation of engineering and operations data in one interface. This offers organisations the ability to scale digital twin solutions more flexibly and reduce IT overhead.
At this year’s Schneider Innovation summit, AVEVA is showcasing its solutions and vision for its industrial digital twin.
For AVEVA Asset Information Management, the new enhancements will bring together trusted asset contexts, accessible through the CONNECT visualisation offering a single flexible and unified UI to visualise trusted engineering, asset and maintenance data. From P&IDs, drawings, and documents to real-time sensor readings, process events, and historical performance metrics, teams can view and analyse all relevant data in one place.
Meanwhile, AVEVA PI Data Infrastructure is an ever-advancing modern and flexible foundation for rapidly connecting, contextualising and acting on industrial insights from operations data. Its sophisticated data management capabilities continue to drive value across enterprises and new enhancements ensure enhanced hybrid connectivity, visualisation and analytics for AVEVA’s industrial digital twin.
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by Gary Mintchell | Jul 8, 2025 | Asset Performance Management, Process Control
Process automation companies like sending press releases when they get a new project. This news in interesting on two fronts. One, it details the use of drones and robots for monitoring and maintenance. Two, this details Yokogawa and Shell working together on the project.
Yokogawa Electric Corp. announced that it has formalized a long-term agreement with Shell Global Solutions International B.V. (“Shell”) to integrate and further develop technologies for utilizing robots and drones in plant monitoring and maintenance. Under the agreement, Yokogawa will add an advanced machine vision tool called Operator Round by Exception (ORE), developed by Shell, into its own OpreX Robot Management Core. The enhanced software service will be made available by Yokogawa to customers in the energy, chemicals, and other industries.
ORE is a digital solution that uses machine vision and AI analytics to enable robots to autonomously perform a number of tasks in the operator round process, such as reading gauges and checking for leaks and machinery issues. It is the result of a two-year collaborative effort within Shell, which combined machine vision strategy with deep capabilities in the field of integrity management, remote site inspection, and corrosion management.
OpreX Robot Management Core is a key product in Yokogawa’s robot solutions. The software helps customers maintain their facilities in a safer and more efficient manner by integrating the management of various types of robots that perform plant maintenance tasks conventionally carried out by humans. When connected to a plant’s control and safety systems, the data acquired can be used to issue instructions to robots, thus enabling the first step to be taken toward autonomous plant operations. The addition of Shell’s ORE technology will significantly increase the number of use cases available to customers through OpreX Robot Management Core.
Moving forward, Yokogawa robotics operations will deploy at two Shell facilities as a pilot into how robotics and drones can deliver value through efficiencies in plant monitoring and maintenance.
This collaboration is the first key milestone for Yokogawa working alongside Shell in the collaboration space at the Energy Transition Campus Amsterdam, which was created by Shell in 2022 to provide a platform for collaboration between companies, societal organizations, governments, and universities to work on tomorrow’s energy solutions. Shell and Yokogawa have also agreed to collaborate on an aligned R&D roadmap to further develop and enhance the machine vision technology, ensuring continuous innovation and improvement. This collaboration underscores both companies’ commitment to providing cutting-edge solutions to the energy and industrial sectors.
by Gary Mintchell | Feb 24, 2025 | Asset Performance Management, Data Management, Process Control, Workforce
I have had a busy month. Good think I didn’t take four days to travel to Orlando. I’m wrapping up my last interview from there today. There are a few more pending if the media relations person can find a way to coordinate calendars.
This interview is with Kim Fenrich, ABB Global Product Marketing Manager, Process Automation, PC. He brought up the term “Digital Habitat”—something not found on the ABB website, but still an interesting concept.
The problem statement recognizes new people entering the industrial workforce. Many of these will not have much background in process operations. Meanwhile our digital technologies contain immense amounts of data that could be used to guide operators toward better decisions.
Fenrich brought a concept called Digital Habitat. This is the area alongside the core process control. This core contains monitoring and optimization. It houses process data. The data then gathers at the edge. In the ABB architecture, data at the edge becomes freely available to other applications, such as asset management and optimization.
Not all data is created equally. Some are “dirty” data that must be cleaned before using. Some is good data from trusted sources with solid metadata. These many applications ride atop the system to run analytics, support decision-making, optimize operations. Sometimes operators are new lacking operations experience and knowledge. Data science to the rescue to clean up and provide interfaces to support these new workers. Sometimes the data science supports engineers working in maintenance and reliability performing predictive analytics or enhancing asset management.
ABB had a suite of applications called the Augmented Operator. The system does pattern mining. Perhaps the operator sees something new. They can ask the system, “Have you seen this before? If so, what happened and how was it resolved?” This greatly helps the younger generation operator.
Should the situation be new to the system, then it can run simulations to predict outcomes and resolutions.
In short, the system:
- Freeing up operators time for more meaningful work such as using data and advanced analytics to optimise processes for energy efficiency and carbon emission savings.
- Enabling early warning of potential failure with AI-powered systems that can use real and historic data to offer troubleshooting solutions, much like a virtual assistant.
- Workflow simulation to check outcomes and for training and augmented reality (AR) headsets to access experts working offsite.
This is from the ABB web site. The next step to achieve this reality is to fuse together the Distributed Control Systems’, operations technology and real-time control system with the Edge and newer IT technology, such as machine learning and AI. As well as incorporating historical data and the mining of other data sources for pattern recognition and knowledge extraction. This will shift the automation system beyond only real time control to one that allows the operator to augment operations from day-to-day. It will be a journey, but humans working with technological systems to augment their cognitive capabilities can amplify their potential and provide huge value to both the workforce and the industry at large – as well as attract new generations to the sector.
by Gary Mintchell | Feb 6, 2025 | Asset Performance Management, Generative AI
AI contributes at least 50% of the content of news in my area of interest. No surprise that Yokogawa has harnessed some AI expertise for its Asset Performance Management.
Yokogawa Electric and UptimeAI announced a strategic agreement aimed at enhancing asset performance management in industrial plants. The agreement is underscored by a capital investment in UptimeAI by Yokogawa.
Under the agreement, the companies will integrate UptimeAI’s AI-powered platform into Yokogawa’s OpreX Asset Health Insights service. The combined solution will provide customers in the oil and gas, chemicals, cement, power, and renewable energy industries with a seamless and powerful approach to optimize plant operations, reliability, and maintenance.
Specifically, the bundled offering will merge the capabilities of OpreX Asset Health Insights as an OT/IT data enablement engine with UptimeAI’s flagship modules, “AI Expert: Generative AI” and “AI Expert: Reliability & Process,” bringing advanced LLM-based AI agents, subject matter knowledge, self-learning workflows, maintenance analysis, and industrial asset library models into a comprehensive AI assistant for plant operators. This solution will enable users to achieve a significant positive return on investment in a short period of time by reducing maintenance and operational costs with predictive insights, root cause analysis, and recommendations driven by automated learning processes.
