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12:06 am
May 12, 2017
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White Paper | Data Acquisition Urgency for Legacy Petrochemical Plants

white paper cover petrochemical industry

Emerson Automation Solutions’ White Paper, titled, “4-Step Roadmap to Top Quartile Performance.”

Subject matter experts leaving, a lack of data knowledge in key areas of the plant and aging equipment can be thorny challenges in optimizing legacy, petrochemical facilities. Luckily, advances in sensing and industrial networking are providing identifiable solutions, such as adding sensors via wireless network versus an expensive wired solution of the past.

As part of our IIoT Spring series, MT is providing reference material to help plants and end users with better optimization strategies, even for aging facilities. This week, Emerson Automation Solutions provides the white paper titled, “4-Step Roadmap to Top Quartile Performance: Leveraging IIoT to Achieve Petrochemical Operational Performance.”

The white paper provides these building blocks for industry plants: 1) Identify Areas for Improvement 2) Acquire Data 3) Analyze Data and 4) Take Corrective Action. An interesting part of the white paper, below, includes adding more sensing due to regulations, even though rules may decrease with the current approach:

Compliance with all these regulations requires plants to install more sensors to monitor operations, record data and file dozens, if not hundreds, of reports to satisfy state and local auditors and inspectors. Regulations aim to improve safety and minimize environmental impact. However, without investments in new sensor technologies and automation, regulations can suppress efficiency and a plant’s ability to meet performance metrics.

Click Here to Download the White Paper >>

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3:55 pm
May 4, 2017
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Fluke Accelix Merges Data Silos

The new Accelix platform, introduced today by Fluke Corp., Everett, WA (, connects the company’s eMaint cloud-based CMMS with its Fluke Connect Wireless test tools and Fluke Condition Monitoring. The key development is that the platform eliminates manual digital entry, allowing reliability and maintenance professionals to quickly gather data and, most important, actually do something with the information.

  • The platform is designed to address issues in several areas:
    There are a third fewer manufacturing facilities today than existed prior to the recession. All are operating at higher productivity levels with smaller teams.
  • Continuous margin pressure has converted maintenance from a cost center to a bottom-line contributor, but maintenance metrics don’t convert to top line KPIs.
  • Increased quality and transparency requirements at all levels.
  • “My team doesn’t speak data. They fix machines. How do I get buy-in?”
  • Younger workers expect answers right away.
  • Small- to medium-sized plants don’t have data scientists.
  • Best people have been promoted to management levels and the team under them is lean and green. Moving forward is hard.
  • There are too many technology choices and not enough obvious ROI.
  • “Taking advantage of IIOT? Not without a WiFi hotspot or cell coverage! We can’t use smart devices on the floor.”
  • “I have so many different legacy proprietary systems I can’t get an overall viewpoint, much less standardize or scale.”

Accelix makes maintenance activities and data visible and connects the reliability and maintenance team to the rest of the enterprise. Because it’s SaaS (software as a service), the platform is accessible and targeted at small- and mid-sized manufacturing operations. According to Kevin Clark, Strategic Alliances Director, Fluke Digital Systems, “Our culture is ready for this. They want data right now.”


4:23 pm
April 25, 2017
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Vibration Machine Learning from the Industrial Internet Consortium

machine learning architecture for CNC machines

Figure 1: The elements and the connectivity being utilized to develop and provide updates to the production system.

Some industry analysts aren’t happy with overused buzzwords like “machine learning” or even “deep machine learning” taking the place of “IIoT” in the hype category. I agree these new buzzwords are ubiquitous in many media corners and deep machine learning is mostly found in R&D.

However, a white paper or deep dive is a great way to see what is possible for predictive analytics in the field or factory. A new white paper from the Industrial Internet Consortium, titled, “Making Factories Smarter Through Machine Learning,” offers a great read on how machine learning can allow for better edge analytics, reduce data streams and promote better data fidelity.

The white paper examines the ability of CNC machines to reduce data streams via machine learning with the use of the Plethora IIoT platform and system-on-chip engineering (SoC). The SoC technology allows for customized software to create application-specific requirements, such as data filtering being sent from machines.

A passage from the White Paper below:

The other capability provided by the software is the ability to read complex sensors and perform pre-processing in terms of data reduction: For example, vibration is sampled at least two times the vibration frequency. In this case, a fast Fourier transform is performed and only the frequency of interest is stored. This is an area where there is high opportunity for more efficient processing – effectively using machine learning for pre-processing and feature selection.

Therefore, it (SoC) can sample each variable with smart criterions: For example, temperature may not be measured with the same frequency of vibration

The white paper provides a real roadmap solution on how to move from preventive maintenance to SoC machine learning and industrial networking solutions. The link to the white paper can be found here.

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6:36 pm
April 13, 2017
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IIoT Trends: Pilot Projects And Silicon Valley

By Grant Gerke, Contributing Editor

The Internet of Things is changing the maintenance and reliability world. Keep up to date with our ongoing coverage of this exciting use of data and technology at

The Internet of Things is changing the maintenance and reliability world. Keep up to date with our ongoing coverage of this exciting use of data and technology at

The Internet of Things (IoT) presents an interesting picture with regard to the consumer and industrial segments. The former could be described as limping while industrial applications are moving towards market acceptance. 

Industrial manufacturing, due to a focus on equipment and networking standards for the past 20 years, is seeing the fruits of its labors through increasing use of predictive analytics. One trend emerging in 2017 is the implementation of smaller IIoT (Industrial Internet of Things) pilot projects that can provide quick results. HIROTEC, a Tier-One supplier to global automotive companies, is an example.

Justin Hester, senior researcher in the IoT Lab for HIROTEC Corp., Hiroshima (,, described his company’s pilot IIoT implementation in a Mar. 2017 National Association Manufacturing (NAM) webinar. “Another way to look at it is I want to crawl, walk, and then run,” he said. “I want to start visualizing data and don’t even want to do augmented reality or predictive analytics yet. That’s where I need to go, but the company can’t make that jump from no IoT solution to a full, augmented reality and predictive analytics tomorrow.”

For the SCRUM project, the automotive supplier and machine-tool maker started with eight CNC-based machines at its Detroit facility in 2015.

The project involved the use of Kepware KEPServerEX from PTC (Needham, MA, and OPC UA network solution to move variable CNC-machine control data to the ThingWorx (Exton, PA, IoT platform to produce real-time data analytics for executives, plant managers, and technicians. Hester noted that the control platforms and data types varied greatly at this business unit, which was one of the reasons for the pilot project.

“Project flexibility is needed, especially when we’re talking about IIoT technology,” stated Hester. “If you have this long lead time with a pilot-project implementation, you learn what matters with the organization. If you have a long, two-year project, you’re stuck.”

HIROTEC’s SCRUM pilot project began with eight CNC-based machines at the company’s Detroit facility.

HIROTEC’s SCRUM pilot project began with eight CNC-based machines at the company’s Detroit facility.

HIROTEC’s application is quite common in industrial manufacturing, and one of the drivers for success is the OPC UA networking platform. This technology allows manufacturers to hold on to legacy equipment and use data from those machines, something that keeps CapEx costs down.

As more interoperability data solutions mature, the space is seeing more entrants, including, recently, Silicon Valley-based called Element Analytics Inc., San Francisco ( This company is targeting predictive analytics for the continuous-process industries. In Jan. 2017, it formally unveiled its Element Platform. The platform takes unstructured, operational sensor data from production “silos” to a cloud-based system, where asset models help predict equipment downtime.

Utilities (specifically wastewater operations) represent yet another segment that seems ripe for IIoT applications.

According to Jim Gillespie, co-founder of Gray Matter Systems, Pittsburgh (, a new Software-as-a-Service (SaaS) tool called ClariFind will alert utilities on sludge overflow failures and also predict thickening failures related to effluent not settling correctly.

How do such operations pay for new sensing technology, equipment, and better communication infrastructure?

In a recent blog post on TechCrunch (, Gillespie pointed to “utilities selling solutions to other wastewater operations as the power industry has done.” As an example, he described how the Washington D.C. Water and Sewer Authority recently commercialized its intellectual property and provided a new revenue channel in the process.

Such reports are snapshots of industrial trends in IIoT and predictive analytics. More success stories are coming. Maybe consumer players will take note. MT


11:04 pm
April 6, 2017
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Big Data | Merge Control and Maintenance Data for Better Efficiencies

The IIoT framework shows how industrial data moves to the cloud for this Agile pilot project.

The IIoT framework shows how industrial data moves to the cloud for this Agile pilot project.

There’s an interesting Q&A post on Big Data, change culture, and control and maintenance data via a recent blog post from ARC’s IIoT Viewpoints site. The discussion includes Vish Avasarala, Co-founder of Saint Software Consultants, and Kenneth Smith, General Manager, Energy at Hortonworks.

There’s an interesting point about industrial manufacturing’s lack of flexibility, in general, and the challenges to change a work culture. I agree with this sentiment, in general, but the Agile project approach may help ignite cloud initiatives with some conservative manufacturers.

In the April Maintenance Technology print issue — and soon on — I write about HIROTEC’s condition monitoring pilot project that would fall under the Agile category. HIROTEC, a Tier-One supplier of exhaust systems for automotive OEMs, recently produced a six-week agile pilot project at their Michigan facility.

The goal was to show results quickly to management, albeit a small sample — eight legacy and new CNC machines.

Justin Hester, from HIROTEC, discussed the condition monitoring/cloud analytics pilot project in a recent National Association Manufacturing webinar:

HIROTEC argues, let’s do a small project. Let’s do a project that’s only six weeks long. It gets people excited. It’s not something that’s going to drone on for the next two years, where they have to devote the next two years of their life to as well as these other requests that will come in from the organization. They see light at the end of the tunnel already.

Hester added, “the Detroit pilot application was a success and HIROTEC is moving forward with IIoT pilot projects in Japan that deal with quality manufacturing.”

Click here to read the ARC interview >>


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3:03 pm
April 4, 2017
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IIoT Offering from ABB and the B&R Acquisition


Last month I discussed how IIoT devices and strategies are taking shape in the water and wastewater industry with a recent survey predicting a $20 billion investment for meters, data management, and analytics in the next eight years. Smart water is getting a lot of attention and some analysts think new business outcomes — see Joe Barkai podcast — are emerging, such as water municipalities  commercializing IP technology.

ABB recently announced a new, digital asset management initiative called Ability and it will aim for the utility and continuous process space, among others. The company emphasized their asset management platform and the analytical power of Microsoft’s Azure platform. The recent press release says the company is targeting many different industries, including oil and gas, water and wastewater, commercial building and even electric vehicle charging networks.

>> More || Smart Water Infrastructure Continues to Grow, but Real Challenges Persist

ABB’s press release on Ability:

ABB Ability helps customers in utilities, industry, transport and infrastructure develop new processes and advance existing ones by providing insights and optimizing planning and controls for real-time operations. The results can then be fed into control systems to improve key metrics such as factory uptime, speed and yield.

(** As I write this post, ABB just announced that it acquired B&R Automation, and will use these new assets to build on their Ability IIoT-platform and pursue factory manufacturing opportunities.)

For more information on the IIoT platform from ABB, click here.

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here! 


2:59 pm
March 8, 2017
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RDI Technologies’ Iris M Lets You See Subtle, Yet Harmful, Machine Motion

Screen Shot 2017-03-08 at 9.05.22 AMRDI Technologies (Knoxville, TN), says “seeing is believing” when it comes to the company’s Iris M powered by Motion Amplification video-processing product and software package. The patented technology measures subtle machinery motion (including deflection, displacement, movement, and vibration) and amplifies that motion to a level that’s visible to the naked eye (see example application video). Every pixel becomes a sensor, creating millions of data points in an instant.

According to RDI, the user simply has to point the camera at an asset, obtain the video data, and push a button to amplify the true motion of the entire field of view. By drawing a box anywhere in the image, he/she can then measure the motion with a time waveform and frequency spectrum.

Editor’s Note: A recently released Stabilization Update software module for the Iris M powered by Motion Amplification package allows users to  stabilize video that contains motion from camera shake due to environments where ground vibration is unavoidable (see video). In addition to automatically stabilizing based on the entire image, this update features an option to draw a Region of Interest (ROI) in the image that the user knows to be stationary. This helps in complicated motion environments.

For more information, CLICK HERE.


12:45 pm
March 8, 2017
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Smart Water Infrastructure Continues to Grow, but Real Challenges Persist

smart water markets

The US (39 projects) and the UK (21 projects) were the most active smart water markets during the last half of 2016. Source: Bluefield Research

By Grant Gerke, Contributing Writer, IIoT

A new report from Bluefield Research suggests that a massive smart infrastructure buildout is coming to the water and wastewater industry in the next eight years, with more than $20 billion to be spent in metering, data management, and analytics.

As devices, sensors and cloud solutions become cheaper over the next ten years, there will be a solid investment in this space but the research rings a little hollow to me. The U.S. industry, in particular, is aging and resources are limited but the big challenge may be in the area of system integrators. In a feature article from a couple years ago, I interviewed Roger Knutson, public works director at the biggest water and wastewater department in Minnesota. For Knutson, the real challenge was in overseeing software and plant monitoring upgrades to multiple plants with his own internal staff. System integrators weren’t in the budget.

“So, the real challenge is to maintain the different technologies during that timeframe,” says Knutson. We’re talking about the new and old versions of software running side-by-side at different plants or just at different plants.”

Even the Bluefield research report says that “a significant hurdle will be integrating legacy systems with new software platforms.” However, the challenge may be workflow processes, the less glamorous side of the asset management and IIoT narrative.

Other highlights from the research include:

• Halving non-revenue water– leaks and billing errors– and reducing energy consumption from 20% to 40%.

• The smart water sector is expected to scale to $12 billion in the US and $11 billion in Europe by 2025. Other hotspots for smart water activity include Australia, Singapore and Israel, where water stress and established utility network operators are more receptive to advanced technology adoption.

• European utilities are at the forefront of smart water in terms of operational solutions, while the US leads in terms of metering.

1601Iot_logoFor more IIoT coverage in maintenance and operations, click here!