Author Archive | Grant Gerke

74

4:29 pm
May 17, 2017
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SAP Chemical Customers Looking for Platform Solutions

Maintenance missteps in chemical-pumping applications can be catastrophic.

Chemical-pumping in action.

There’s a lot of talk about the “things” in the Industrial Internet of Things formula, but analytical platforms are also very integral parts to this complex solution. Return on investment (ROI) or even total cost ownership (TCO) metrics are key ingredients for many manufacturers in realizing the justification for a digital transformation for a plant.

Recently, SAP chemical manufacturers discussed platform and investment at the Best Practices for Chemicals event in Houston. Mark Sen Gupta, from ARC Advisory Group’s Industrie 4.0 blog, recently wrote a post, titled, “Cloud Adoption Slow and Steady Among SAP Chemical Users.” The post outlines the urgency for chemical plants to modernize and connect their assets to transform legacy facilities.

Here’s an excerpt from the post:

Companies are at different stages regarding cloud adoption readiness. Some companies have a clear Cloud strategy; some are in the process of moving their test systems into the cloud, others are still waiting. Overall, though, attendees agreed that moving to the cloud should be part of system migration considerations targeting at Total Cost of Ownership and complexity reduction through standardization.

The takeaway for me is more learning is needed by plant managers and executives to better understand platform solutions and possible new business outcomes, beyond asset management. SAP has two resources to help in this area, the Leonardo Portfolio and a Digital Transformation Navigator tool. The Leonardo Portfolio advertises its ability to “bridge things with processes” but also new business processes and business models.

For more information on Leonardo Portfolio, click here, and more about the Digital Transformation Navigator Tool.

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70

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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30

4:19 pm
May 4, 2017
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Technology Brief | Better Monitoring for Pneumatic Machines

The spring always provides inspiration to expand our knowledge base and Maintenance Technology delivers another deep dive on the importance of equipment monitoring from a pneumatic Original Equipment Manufacturer (OEM) perspective. Aventics Corp., the former Bosch Rexroth pneumatics business unit, describes some of the objectives end users should consider with machinery in the IIoT world.

Excerpt from Design FAQs around the Internet of Things:

To properly collect data in pneumatic systems, a combination of not only hardware but also electronics and analyzing software is necessary. Increasing the volume of data transfer, however, stresses controls and IT networks. Having local data analysis can help ease the strain on systems. IoT systems must create value-add for the user in the ways of predictive maintenance, energy savings, efficiency earnings, and ease of use.

Download the Technology Brief >>

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72

6:29 pm
April 25, 2017
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White Paper | Making Machines Smarter Through Machine Learning

1704iicwpA new white paper from the Industrial Internet Consortium (www.iiconsortium.org) , 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.

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, SoC machine learning and simple industrial networking solutions to make this happen. The link to the white paper can be found here.

Download the White Paper >>

Industrial Internet Consortium
http://www.iiconsortium.org/

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158

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.

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

71

7:50 pm
April 14, 2017
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Engine OEM Identifies New Business Service

160720catlogoDisruption is an overused word in technology, but Joe Barkai’s tagline to his book about IIoT says it all: How the Industrial Internet of Things is Changing Every Business. For Mak, a supplier of engines to the maritime industry, that means changing their business model to focus and recognize that servicing their large engines remotely isn’t some wild science fiction fantasy. It’s a reality for OEMs as end users move toward IIoT strategies.

The maritime engine supplier is partnering with Caterpillar Marine Asset Intelligence (www.cat.com) and will create a condition monitoring approach for the first project. This project includes an M46 DF dual-fuel engine and will provide real-time monitoring on the ship.

“This effort enables operations and maintenance leaders to make better decisions using data and analytics, helping to drive reduced cost, downtime and risk,” says Ken Krooner, Technology & Operations Manager for Caterpillar Marine Asset Intelligence.

According to Caterpillar Marine, “the onboard analytics and user interface provide the onboard crew with real-time information, such as the condition of their equipment and what they should do about any potential issues.”

More importantly, the analytics software allows for multi-level reporting.

“At the highest level, there are high-level dashboards and reports which can provide a variety of graphs and data visualizations, including vessel performance curves, efficiency comparisons, custom metrics, geophysical location, says Leslie Bell-Friedel, global business mgr. at Caterpillar Marine Asset Intelligence in an interview for a company publication. At a detailed engineering level, there are simple red-yellow-green indicators for each piece of equipment that summarize the current and projected condition, as well as the ability to drill deep to understand the health and performance of a piece of equipment.”

Also, qualified data can be seen ashore, where additional automated analytics are used to analyze the data — both from an individual vessel as well as from a fleet perspective — and where experts are on hand to review the analytic output and apply their experience to it. Access to the analytics can be done via any web-based device, either onshore or remotely. At this point, there’s no app available.

Click here to read the Bell-Friedel’s interview >>

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127

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 maintenancetechnology.com/iot.

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 maintenancetechnology.com/iot.

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 (hirotec.co.jp, hirotecamerica.com), 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, ptc.com) and OPC UA network solution to move variable CNC-machine control data to the ThingWorx (Exton, PA, thingworx.com) 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 (elementanalytics.com). 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 (graymattersystems.com), 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 (techcrunch.com), 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

ggerke@acceleratedcontent.com

213

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 maintenanctechnology.com — 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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