AI Comes to Asset Performance Management

AI is perhaps the most overused buzz word in our market right now. On my drive down I-90 to Rosemont, IL for the Assembly show this morning, I listened to the first part of an interview on the Tim Ferris podcast with Eric Schmidt. This former Google CEO has written a book just released on AI. AI is short for Artificial Intelligence, a phrase which some of us refer to as neither artificial or intelligent. Schmidt defines it as software that learns. Feed it more data and it learns from it.

This news comes from ABB which has added AI to its launch of Ability Genix Asset Performance Management (APM) Suite for condition monitoring, predictive maintenance, and 360-degree asset performance insights for the process, utility and transportation industries. The quick look is:

  • The launch of ABB Ability Genix Asset Performance Management Suite brings next-generation AI-based predictive maintenance, asset reliability and integrity insights to process and utility industries
  • Genix APM is an enterprise-grade application to monitor assets, prescribe maintenance actions, improve equipment utilization, and support lifecycle analysis and capital planning
  • Solution provides actionable insights into all aspects of asset performance, enabling customers to reduce machine downtime by up to 50 percent

The Genix APM Suite makes it easy to add asset condition monitoring to existing operational technology (OT) landscapes, enables prioritization of maintenance activities based on AI-informed predictions, and provides a comprehensive overview of asset performance.

Genix APM Suite also empowers significant improvements in operational sustainability. By assessing the remaining useful life of industrial assets, Genix APM generates a plan for preventive maintenance, which can extend equipment uptime by as much as 50 percent and increase asset life by up to 40 percent.

With reliable data insights, decision makers are provided with the information required in order to identify gaps and areas of improvement for energy efficiency and tighter control of operations, increasing asset availability and improving profit potential.

“Poor asset availability and reliability is a major problem that results in unplanned downtime and unexpected maintenance costs, and also impedes strategic planning and procurement,” said Rajesh Ramachandran, Chief Digital Officer at ABB Process Automation. “It’s not that industrial customers lack data; it’s that many lack effective ways to use their data to improve operational and business performance.”

Genix APM is built on the ABB Ability Genix Industrial Analytics and AI Suite. ABB Ability Genix is a modular, IIoT and analytics suite, which integrates IT, OT and other enterprise data in a contextualized manner, applying advanced industrial AI capabilities that support new insights to optimize operations.

Future Operations, Maintenance, Reliability Workforce

An old friend and several acquaintances found themselves adrift when a magazine closed. All being entrepreneurial, they started a  website and newsletter—RAM Review (Reliability, Availability, Maintenance). Old friend Jane Alexander is the editor. Not meaning she’s old, just that we’ve known each other for many years.

I met Bob Williamson 10 or 12 years ago mostly around discussions of ISO 55000 on asset management. He wrote the lead essay for a recent email newsletter on workforce. Now, I have to admit that the only part of manufacturing I never worked in was maintenance and  reliability. I did work with skilled trades when I was a sales engineer, though. I considered them geniuses for the way they could fix things. One of the points of Bob’s essay is taking care of things before they break and need help.

The main workforce discussion in media concerns remote or hybrid work. Many engineering roles can be performed remotely. Many roles within manufacturing and production must be performed on site. With the current and projected future labor shortage, I like his closing paragraph except for the put down on current operators. I knew plenty who cared for their machine or process. Of course, many didn’t. Most likely a management failure. But cross-training people to be at least to some degree both competent operators and first-line RAM people seems to me to be a winning strategy. I’ve reprinted most of Bob’s essay below. You can read it on their website.

For many manufacturers, returning to traditional ways of work simply will not be an option. Something must change if they are to attract, hire, and retain a capable workforce. Therefore, I believe technology and desperately willing top-management teams will also help alter work cultures on factory floors. Respondents to the Manufacturing Alliance/Aon survey suggested offering “flexible working hours, compressed work weeks, split shifts, shift swapping, and part-time positions.”  Use of such enticements with plant-floor workforces would look very different than use among the carpet dwellers in front offices.

We have another option, of course: Technology can automate our manufacturing processes, and much of it is far more affordable than it was a decade ago. In fact, given the rising cost of labor over the past decade, with increasing healthcare-cost burdens and skills shortages, many businesses have already automated some of their labor-intensive processes. The times we are in call for—make that scream for—large-scale automation. Yet, while process automation can be easier for large, deep-pocketed companies than for the smalls, it’s still a huge challenge.

There are four big hurdles to be overcome when automating manufacturing processes: availability, installation, sustainable reliability, and work-culture change. And remember, skills and labor shortages are widespread in these post-pandemic times. Moreover, despite the supply chain’s efforts to heal and keep up, manufacturers of automation technologies aren’t immune to the production-barrier ills that others face these days.

To repeat: RAM professionals are on manufacturing’s front line. Skill shortages may be affecting our ranks, but there are recruiting and training efforts underway in many companies to remedy the situation. In addition, we have technologies for carrying out data collection, analysis, and problem-solving somewhat remotely. However, the boots-on-the-ground parts of reliability and maintenance will not be virtual or remote.

So, consider this option: Recruit and train displaced production workers to wear some RAM “boots.”  They’ll be familiar with industrial environments and the importance of plant equipment. Then, let’s train our current production workers to care more for their machines than they did in the past, and, in the process, become the eyes and ears for reliability, availability, and maintenance improvement.TRR

AI plus IIoT Yields Plant Information and Visualization

Companies are sprinkling their press releases and Websites with Artificial Intelligence (AI) like sugar on your cornflakes. Now we even have Artificial Intelligence of Internet of Things—AioT. One of my favorite series of questions these days runs something like: what do you mean by AI; how is it used; what do operators see; what does it do—really?

One outgrowth of a series of meetings from the recent ARC Advisory Group Industry Forum was an interview with serial entrepreneur Mike Brooks. Most recently he was President and COO of Mtelligence (Mtell) when it was acquired by AspenTech a bit over four years ago.

He clued me into a number of Aspen products based on the Mtell technology. More on those below. First, some insights from someone who has witnessed a lot in the industry. 

Talking about machine learning, Brooks told me that it’s not just AI on its own that gives value. Look for a combination of AI plus domain knowledge. This gives you causation, not just correlation. It is also important to build AI from first principles (I’m betting many miss that one). Mostly, AI is a tool for providing event analytics for front line workers. 

I’ll combine Brooks and other sources to describe the more practical AspenTech solutions:

From a blog post by Adi Pendyala, Sr. Director, Market Strategy—Aspen AIoT Hub: The Cloud-Ready Infrastructure for Industrial AI

Artificial intelligence (AI) and the Industrial Internet of Things (IIoT) are two of the most prominent technological forces driving digital transformation for capital-intensive industries today. Collectively, they’re like the body and brain of industrial digital transformation: IIoT is the body, creating and transmitting data from a variety sources that is sometimes acted upon, while AI is the brain, turning data into intelligence for smarter decisions and enabling the digital future of industrial organizations.

The confluence of these technological forces gives rise to a new digital solution category – the Artificial Intelligence of Things (AIoT) – that centers on unlocking the hidden business value in industrial data.

Impact of IT-OT Convergence: Sharp market volatility means that capital-intensive industries have to be more agile than ever before to survive and thrive in every cycle – an area that has thwarted the OT-side of industrial organizations in the past. Enterprises are looking to exploit the rapid convergence of IT and OT to significantly reduce the technological implementation risk and the time-to-market risk for introducing AI-rich, real-time applications to complex industrial operations. The rise of the digital executive, i.e. the CTO/CDO/CIO, in driving the digital transformation strategy of industrial organizations is a key influencer of this trend.

Unlock Industrial Data Value: There is a critical (and growing) need for access to industrial analytics and actionable insights in making business decisions – across all levels of the enterprise. Efforts to mine pools (silos) of data across the enterprise are often stalled by the challenges of data collection and integration, with promised business insights and agility never materializing. Organizations are switching their focus from mass data accumulation to strategic industrial data management, specifically homing in on data integration, data mobility and data accessibility across the organization – with the goal of using AI-enabled technologies to unlock the hidden value in these previously unoptimized and undiscovered sets of industrial data.

Lowering the Digitalization Barrier: Industrial organizations are increasing investment in lowering the barriers to AI adoption by deploying fit-for-purpose Industrial AI applications that combine data science and AI with software and domain expertise. This will be the key to overcome a lack of in-house skills and drastically reduce the need for an army of data scientists. To scale this effort, many enterprises are adopting new measures to reduce complexity in interoperability, overcome information silos and harmonize towards a cloud-ready infrastructure that bridges legacy systems with next-generation solutions.

The Aspen AIoT Hub – Cloud-Ready Industrial AI Infrastructure

The AIoT Hub provides the integrated data management, edge and cloud infrastructure and production-grade AI environment to build, deploy and host Industrial AI applications at enterprise speed and scale. It also serves as the foundational infrastructure to realize the transformative vision for the Self-Optimizing Plant. In fact, as part of our recent aspenONE V12 release, the AIoT Hub provides the underlying cloud-ready, enterprise-scale infrastructure that powers V12 Industrial AI applications such as Aspen AI Model Builder and Aspen Event Analytics.

Key Capabilities of the Aspen AIoT Hub

Data Integration & Mobility 

On average, between 60% and 73% of all data within an enterprise goes unused. This challenge is further exacerbated by the lack of a scalable data infrastructure to power Industrial AI models from training to productization. Through the AIoT Hub, organizations will be able to access and leverage fully integrated data, from sensors to the edge and cloud, across the enterprise.

Cloud-Ready Infrastructure

Scaling AI requires providing the tools, infrastructure and workflows for powering Industrial AI across the solution lifecycle. It also requires the software, hardware and enterprise architecture needed to productize AI in industrial environments, including broader collaboration between development, data science and infrastructure capabilities such as CloudOps, DevOps, MLOps and others. This dimension is critical to helping organizations mature beyond sporadic AI proof-of-concepts to an enterprise-wide Industrial AI strategy. 

Enterprise-wide Visualization

Industrial organizations are seeking to aggregate data from different sources across the enterprise, transforming it into analytics and visualizations to guide better decisions at every business level. The goal is to translate real-time data into faster, smarter, profitable business decisions to visualize deviations, sequences and trends automatically and identify risks and opportunities early. The AIoT Hub enables enterprise users’ access to real-time data and analytics to do all of this – improving collaboration, project efficiency and operations by tapping into the power of accelerated insights and enhanced visualizations.

Industrial AI Applications Ecosystem

Enterprises are looking for purpose-built, fully integrated AI environments for their data scientists to accelerate the transformation from raw data to productized AI/ML algorithms. The AIoT Hub provides an embedded workbench for feature engineering, training and rapidly productizing machine learning (ML) models, as well as supports versioning and collaboration. It empowers data scientists, at customers and partners, to collaborate and build their own data-rich AI apps. 

Hitachi ABB Power Grids’ Digital Enterprise Joins Hitachi’s Lumada Portfolio

About six months ago, ABB completed a divestiture of about 80% of its holding in ABB Power Grid business, and Hitachi acquired it. The new business, a joint venture, is called Hitachi ABB Power Grids. Today, it announced the integration of its Digital Enterprise solution with Hitachi Vantara’s Lumada portfolio of digital solutions and services for turning data into insights. 
 
The two Hitachi business entities have agreed to rebrand the DE components as Lumada Asset Performance Management (APM), Lumada Enterprise Asset Management (EAM), and Lumada Field Service Management (FSM), adding to the growing portfolio of DataOps and Industrial IoT solutions. 
  
The DE portfolio of solutions and its predecessors enable customers spanning multiple global industries to operate, analyze and optimize over $4 trillion of assets every day. With the incorporation of the DE portfolio into Lumada, this experience is further complemented by a leading technology engine to deliver access to information, systems, people and analytics across asset-intensive organizations. 
 
With Digital Enterprise’s incorporation into Lumada, Hitachi ABB Power Grids’ energy domain experience will be augmented by Hitachi’s Lumada Industrial IoT platform. Hitachi was recently named a Leader in the 2020 Gartner Magic Quadrant for Industrial IoT Platforms based on Gartner Inc.’s evaluation of the company and its Lumada IoT software. 

“Our software solutions and Lumada are highly complementary,” said Massimo Danieli, managing director, grid automation business unit, Hitachi ABB Power Grids. “Combining best-in-class Lumada IoT capabilities and the domain expertise built into Digital Enterprise applications provides both new and existing customers unparalleled flexibility and faster time to value, while preserving the value of their past software investments. The journey we began with our customers as part of the Digital Enterprise evolution story has become broader and more compelling, as we join the Lumada ecosystem.” 

“Lumada Enterprise Asset Management and Field Service Management allow us to seamlessly expand our Ellipse EAM, enabling us to share information across all parts of our organization, tearing down silos and giving us the opportunity to formulate a longer-term, holistic strategy that reflects our specific business outcomes,” said Brian Green, general manager, asset management, from the Australian Rail Track Corporation (ARTC). “In addition, implementing these solutions allows us to optimize the quality of the data we collect and ensure safe, compliant and efficient business operations.” 

“Bringing these solutions that each encapsulate deep domain expertise into the greater Lumada ecosystem gives customers an extremely powerful combination of tools to modernize their business,” said Chris Scheefer, senior vice president, Industry Practice, Hitachi Vantara. “The holistic view of assets and information provided by Lumada allows leadership to analyze and react in real-time, enabling efficient, effective operations and a foundation to create a more sustainable future.”  

DE and Lumada also share core foundational features: a modern microservices design, vendor-agnostic interoperability, and a flexible deployment model, including cloud, on-premises and hybrid. 
 
With the combination of Hitachi ABB Power Grids’ Digital Enterprise application portfolio and Hitachi’s Lumada solutions offered by Hitachi Vantara, customers will be able to benefit from additional data services including data integration, data cataloging, edge intelligence, data management, analytics and more. 
 
The new integrated Lumada portfolio will offer advantages to customers in the following key areas: 

1.     Digital Transformation & Data Modernization – improving access to and insights from data 

2.     Connected Asset Performance – helping to predict and prevent asset failures 

3.     Intelligent Operations Management – improving oversight and maintenance of assets 

4.      Health, Safety & Environment – enabling safer environments for workers and the public 

Amazon a Predictive Maintenance Supplier with AWS?

Amazon popped up on a recent post regarding Amazon Web Services. This news came to me from the analyst firm Interact Analysis. I’ve talked with executives there a few times, and I generally like the approach they take. I’m amused that IT companies think maintenance when they think manufacturing and then add predictive analytics, which they all have, combining them into predictive maintenance looking for a killer app. 

Anyway, this analysis by Blake Griffin, senior analyst at Interact Analysis, has food for thought. Just what is Amazon up to with the manufacturing space?

  • “The full development of Amazon’s industrial digitalization offering represents the first time a supplier has the ability to provide both the cloud storage and analytic capabilities under one entity”
  • “If customers are looking to utilize the cloud for their industrial digitalization initiatives, Amazon would represent the fewest number of touchpoints between customer and supplier during the sales process”
  • “Additionally, many manufacturers may already be using AWS for cloud storage but have yet to invest into further industrial digitalization technology. In these scenarios, Amazon would already have a ‘foot in the door’”
  • Amazon offers an on-premise version of AWS for low latency applications – AWS Outpost: “In our opinion however, outpost will also serve as an option for customers looking to implement predictive maintenance who may be shy of hosting their operational data on the cloud… AWS Outpost will be regularly updated and patched… which ensures that users are still able to take advantage of the scale at which AWS operates”

On December 1st, 2020, Amazon announced a suite of new AWS machine learning services. To many, this announcement appeared to be Amazon’s launching off point towards being a major supplier of predictive maintenance solutions. However, this announcement follows a long history of Amazon carving out its capabilities in industrial digitalization. Since the ecommerce behemoth’s 2018 release of AWS IoT Sitewise, a service which enables its users to gather and organize asset health related data housed in repositories such as a historian, Amazon has consistently added to its industrial digitalization offering.  Now, the company has a highly competitive solution with one capability completely unique to Amazon.

Amazon’s Industrial Digitalization Offering Has Been Developing for Years

In some ways, Amazon’s announcement of its new suite of machine learning services represents a rounding out of a predictive maintenance offering rather than a jumping off point. When manufacturers are looking at implementing predictive maintenance into their facilities, they are asking these fundamental questions:

  1. Which assets do I have visibility into already? How can I leverage this data?
  2. Which assets do I not have visibility into? What can I do to change that?

The announcement of AWS IoT Sitewise was Amazon’s solution to the first question. Many manufacturers in process industries generate large amounts of data from the devices controlling their machines. This data is often stored in a historian and without the tooling necessary to effectively manage and analyze such data, much of its value can be lost. AWS IoT Sitewise was developed so manufacturers could more effectively utilize this data for condition monitoring/predictive maintenance purposes. The solution is deployed through software housed in a gateway which then communicates the collected data to the AWS cloud. In our opinion, this marked Amazon’s true entry into the predictive maintenance market. Strategically, this was a logical first move. Amazon already had a wealth of analytical tools it could deploy to make use of data housed in a historian, the only thing needed was a mechanism for gathering and organizing that data to be analyzed.

Fast forward to Amazon’s recent announcement and we see the company moving to provide a solution to question two. One asset that is cited often as being “offline” from a condition data perspective are the mechanical portions of a motor driven system i.e. induction motors, gearboxes, bearings blocks, etc. These components are numerous throughout factory floors and their failure can represent significant loss of production if they are part of an application critical process. The industry has responded to this need by offering smart sensors, a wireless enabled sensor which can be connected to the side of a motor for purposes of gathering data on vibration and temperature behavior. These two data points, when combined with machine learning algorithms, can quickly illuminate what kind of stress motor components are facing and alert its users of problems ahead of failure.

One of the services announced in late 2020 has been coined Amazon Monitron. The solution utilizes smart sensors and gateways produced by Amazon to offer up data on the health of motor system equipment; effectively solving the problem of gathering data on assets not being monitored via historian data. This solution is in direct competition with predictive maintenance providers like ABB, Siemens, SKF, etc. In our view, the announcement of Monitron means Amazon now has a solution which fully addresses the needs of manufacturers looking to invest in predictive maintenance as part of a broader industrial digitalization initiative. Amazon’s utilization of data housed in a historian, combined with its smart sensor offering and vast analytics capability offered through AWS, make this solution as competitive as any on the market. Amazon does however have one distinct advantage over competition however: being a provider of cloud storage.

Amazon’s Unique Capability: Cloud Storage Ownership

Every platform offered by the major providers of predictive maintenance are built on cloud storage technology offered largely by either AWS or Microsoft Azure. ABB Ability cites Microsoft Azure as the landscape in which Ability operates. Similarly, Schneider Electric’s Ecostuxure platform utilizes Microsoft Azure. Siemens Mindsphere has developed the capability to be used with either AWS or Azure, announcing its compatibility with the latter in 2018. The full development of Amazon’s industrial digitalization offering represents the first time a supplier has the ability to provide both the cloud storage and analytic capabilities under one entity.

It is difficult to foresee what impact this will have on the partnerships AWS has in place with current industrial digitalization providers. What is easy to see however are the numerous advantages Amazon will have in potentially winning the business of those investing into industrial digitalization for the first time. If customers are looking to utilize the cloud for their industrial digitalization initiatives, Amazon would represent the fewest number of touchpoints between customer and supplier during the sales process. Additionally, many manufacturers may already be using AWS for cloud storage but have yet to invest into further industrial digitalization technology. In these scenarios, Amazon would already have a ‘foot in the door’ which would yield them an advantage when the time comes for users to begin evaluating providers of digitalization.

Amazon’s AWS Outpost Helps Overcome a Major Barrier to Predictive Maintenance Adoption

One of the largest barriers facing suppliers of predictive maintenance solutions is manufacturers’ reluctance to host its operational data on the cloud. Recently, Interact Analysis partnered with the Association for Packaging and Processing Technologies (PMMI) to produce a white paper and accompanying survey pertaining to adoption of predictive maintenance technology within the packaging industry. The whitepaper and survey results are available for download for free via this link. One of the questions asked in the survey looked at the adoption of predictive maintenance within OEM and system integrator offerings. “Our customers will not allow remote access to their machinery” received the second highest weighted score according to the survey.

Question: To what extent are the following statements describing the adoption of predictive maintenance (PdM) technologies at your company, true or false?

  1. We are not familiar with PdM technology.
  2. The added cost of PdM technology is too high to justify.
  3. We do not want to have to pay for an ongoing subscription to access sensor data from an automation vendor.
  4. The technology is too new.
  5. We currently offer machines with PdM technology
  6. None of our customers have expressed interest in PdM technology
  7. Our customers will not allow remote access to their machinery (remote monitoring)

This hesitancy by users to allow access to operational data has led suppliers to develop solutions which, instead of aggregating and analyzing data in the cloud, host their data for analysis onsite.

Amazon has addressed this concern by offering an on-premise version of AWS. This on-premise version of AWS, termed AWS Outpost, was released in 2019 and is designed to serve applications requiring low latency. In our opinion however, outpost will also serve as an option for customers looking to implement predictive maintenance who may be shy of hosting their operational data on the cloud. Keeping data onsite as opposed to in the cloud ensures the door to the OT network remains closed; something many manufacturers are keen to maintain.

Having the power of a modular cloud system like AWS on-premise is an incredibly powerful development in the predictive maintenance market. AWS Outpost will be regularly updated and patched by a regional AWS team which ensures that users are still able to take advantage of the scale at which AWS operates. This is an important consideration when working with machine learning algorithms which become more accurate when deployed at scale. Current on-premise predictive maintenance solutions sacrifice this accuracy in favor of the increased security which on-premise brings. With AWS Outpost, users will no longer have to make that sacrifice. 

Additionally, if you define an edge device as the point at which data is pushed to the cloud, this solution effectively eliminates the need for such devices thus simplifying the overall architecture.

Final Thoughts

At the very least, this announcement should be taken as a signpost of future growth within an already fast-growing predictive maintenance market. Amazon does not enter markets which are expected to appreciate modestly; it enters markets whose opportunity could one day be worth billions of dollars. The amount of time spent developing, releasing, and improving upon Amazon’s industrial digitalization offering should be indicative of the faith the company has in the future of this market.

Cloud Partnerships and Gas Metering

Honeywell’s PR person has been a regular in my inbox for the past few months. I have two news releases from different areas of Honeywell, but each relevant to the cause. One fits within the current trend toward cloud partnerships that extend value to customers. The other advances gas metering—eliminating a source of waste and cost.

Honeywell and Microsoft

The integration of Honeywell Forge and Microsoft Dynamics 365 Field Service will provide closed loop maintenance for building owners and operators.

The integration allows customers to access operating data that includes workflow management support so that workers in the field will be able to access critical data that will help them prioritize, analyze, and solve problems more quickly.

The first area of focus will be in automating maintenance for building owners and operators. To optimize their buildings’ energy, performance and comfort, they often need to pull data from a variety of sources that are not normalized and inform remote and dispersed workforces. Facility managers must determine what problem to fix, when to fix it and who to assign to the job, which can be very difficult without having the necessary asset know-how and work order management capability.

“Honeywell’s partnership with Microsoft will deliver new value to our customers as we help them solve business challenges by digitizing their operations,” said Que Dallara, president and CEO, Honeywell Connected Enterprise. “Working with Microsoft, Honeywell will bring solutions at scale – powered by AI-driven insights and immediate access to data – that will help our customers work more efficiently than ever before.”

Dynamics 365 Field Service allows companies to remotely detect and address potential issues early to avoid unnecessary downtime or operational inefficiencies by analyzing IoT data and to improve proactive service offerings through AI-infused IoT alerts and work orders. Leveraging Microsoft’s cloud will enable Honeywell to quickly bring new offerings to market while helping customers meet regional security, privacy, and compliance requirements.

“To achieve resilient operations and sustainable growth, businesses need to partner to fully unlock the opportunities of cloud, AI and IoT technologies. By integrating Honeywell and Microsoft services, companies turn IoT data into critical business insights and actions to optimize operations and deliver new customer value faster,” said Judson Althoff, executive vice president, Microsoft’s Worldwide Commercial Business.

Working with Microsoft, Honeywell is already delivering the following Honeywell Forge SaaS solutions that will address what customers need now to return to work and, in the future, to operate safely and efficiently, including:

  • Digitized Maintenance – Offers a panoramic view of the performance of facilities and assets using near real-time analytics. This provides important information about critical equipment issues before they become big repair or even replacement problems.
  • Energy Optimization – A cloud-based, closed-loop, machine-learning solution that continuously studies a building’s HVAC energy consumption patterns and automatically adjusts to optimal energy saving settings without compromising occupant comfort levels.
  • OT Cybersecurity – Honeywell Forge Cybersecurity provides continuous threat detection with minimal disruption of services. The robust software solution simplifies, strengthens and scales industrial cybersecurity operations across the enterprise.

The companies are also exploring more ways to bring innovation to customers by integrating Honeywell Forge solutions with Azure services such as Azure Digital Twins and Azure edge capabilities. Using edge computing, customers can run AI, machine learning, and business processes directly across plants, warehouses, machines, and appliances for quicker actions without the need for a constant internet connection.

Honeywell Metering Software App

Honeywell announced the release of Measurement IQ (MIQ) Optimize – an enterprise-wide solution for monitoring meters, gas chromatographs, and other measurement assets. The new software solution collects and analyses diagnostic information from devices across different sites for a real-time overview of metering health. The MIQ Optimize solution can provide an early warning of measurement concerns, so that operators can prioritize and address meter issues having the most impact on their business.  

MIQ Optimize allows users to detect increasing measurement uncertainty more quickly, helping avoid failures and downtime, reducing engineer site visits, and extending calibration periods. The solution also includes recommended actions to ease troubleshooting and maintenance – identifying the most likely causes of meter issues and suggesting remedies. The software supports Honeywell meters and gas chromatographs as well as devices from other major manufacturers. 

“Measurement IQ Optimize provides the first vendor-agnostic, enterprise-wide view of the health of metering operations. From a single pane of glass, users can identify their key sources of measurement uncertainty, the value at risk and what they can do to reduce it,” said Max Gutberlet, offering manager, gas software and solutions, Honeywell Process Solutions. ”It’s a powerful tool for directing condition-based maintenance programs and dramatically reducing the cost of lost and unaccounted gas through mismeasurement.” 

The solution allows gas distribution businesses and others to address the millions lost every year to undetected meter inaccuracy. Uncertainty of just 0.5% in ultrasonic gas measurement can add up to a cost of approximately $1 million per year. With real-time monitoring, operators can detect issues before these losses accumulate; while enterprise-wide management of meter uncertainty can drive improvement in fiscal metering margins.