Food Industry Interest is Growing in the Industrial Internet of Things

But security concerns and clear understanding of ROI slows infrastructure development.

By Kevin T. Higgins, Managing Editor

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Machine assets, not human assets, are the focus of most IIoT initiatives, however. For the process engineer, real-time data exchange between automated machines is the issue, with remote access through the internet to performance reports a residual benefit. The Big Data proxy for Google is the cloud or, possibly, OEMs in the role of Pretty Big Data.

Analyzing controls data from multiple machines that are identical or very similar can yield much more powerful information on machine condition and process performance than data from a single machine. If the database included hundreds of machines, regardless of ownership, in dozens of processing environments, it would be even more powerful.

The likelihood of that scenario, however, hovers around nil: machine performance data is jealously guarded. For years, OEMs and skid builders have provided remote diagnostic capabilities in advanced machine controls. If those diagnostics ran continuously in the background, the IIoT could be the conduit to vendor-supported alarms and analytics.

But the very idea of an internet portal to a plant’s Ethernet communications sends a shudder down the spine of IT. Instead of easier access from outside the plant, food manufacturers are placing more restrictions. “One word: firewalls,” summarizes Ola Wesstrom, senior industry manager-food and beverage for instrument supplier Endress+Hauser (www.us-endress.com), Greenwood, Ind. Expect more restrictions, not less, to allowing third parties to listen in on machine performance.

The Shadow knows

Data security is the obsession of IT and, to some extent, executive management. Engineers and operations personnel, on the other hand, are more receptive to IIoT collaboration.

In the Food Processing-ABB automation survey, plant operations professionals were twice as likely to view remote access to machine controls favorably as C-suite executives. They also had a much more favorable view of vendor access to controls data and the connection of field devices to a wireless network.

A possible workaround to security concerns is creation of a parallel controls network, a system of “shadow sensors,” in the words of Rob McGreevy, vice president-operations, information & asset management at Schneider Electric (www.schneider-electric.com), Andover, Mass.
“Low-cost sensing and other technologies allow engineers to enhance performance monitoring in a fraction of the time and cost,” he explains.

“Shadow sensors costing $200 or $300 each could sit on top of the high-fidelity, deterministic controls needed for high-speed machines.”

A handful of these wireless devices would communicate via Bluetooth to the cloud and monitor machine condition, product quality and other factors. “It’s not that complicated and doesn’t require pulling wires and costly systems integration,” he adds.

Another avenue to better process control and improvement in yield and product quality passes through in-line inspection systems. The performance of upstream machinery is inferred in the products being inspected. While inspection equipment is only required to render a pass/fail decision, it often has the computing power to do much more.

An example is the software suite that Key Technology Inc. (www.key.net), Walla Walla, Wash., began embedding in high-speed optical sorters two years ago. The immediate benefits relate to the quality of raw materials, but there also is potential for improved process control and machine performance.

“Optical sorters could be looked at as digital information centers,” observes Marco Azzaretti, who oversees Key’s advanced inspection systems. Images of each item moving down the line are captured, and real-time processing of the captured data can be used to adjust the process.

Potato processors are at the top of the sophistication hierarchy of fruit, vegetable and nut processors who use Key’s sorters. Their lines run 24/7 up to three weeks between cleaning and sanitation shutdowns. When sorters are placed at multiple points on the line, they tell a story of how the processes between them impacted the product. They also provide clues about upstream machine performance and the state of the sorter itself — a malfunctioning ejector, for example, or a sensor window in need of cleaning.

Machine performance also is an indicator of component wear, and analyzing the performance of multiple sorters in a company’s manufacturing network enhances predictive maintenance. The information would be even more powerful if it consolidated data from comparable sorters at McCain Foods, Simplot and other potato processors, although security concerns pre-empt that possibility.

In food manufacturing, such a database is a pipedream. On the other hand, simulation models built on integrated data from multiple sources might one day help resolve bottlenecks and lead to process improvements for every potato processor.

“Big Data can be a significant enabler,” Azzaretti allows, “but it goes hand in hand with the ability to capture data in real time.” When throughput is measure in tons per hour, the faster the response to change, the less rework and waste.

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