Predictive Maintenance Is Coming to Food Manufacturing

Data collection and analysis from a broader base of installed equipment should help both processors and their OEMs.

By Kevin T. Higgins, Managing Editor

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Machine downtime, by and large, is a random event, which explains why failure prediction remains an elusive goal in food manufacturing.

Predictive maintenance certainly is possible, as practices in other industries demonstrate. The big obstacle is cost: Establishing an in-house predictive maintenance program “can easily exceed $100,000 for start-up alone,” according to authorities at SKF Inc., Lansdale, Pa. It also requires another precious commodity: time. Time-based maintenance routines are not a panacea for random breakdowns, but they are evidence of a plan to reduce their frequency.

The cost of infrared thermometers, ultrasonic probes, vibration monitors and other tools of condition monitoring are declining, although price points still may exceed the budgets of smaller food and beverage manufacturers. Smart sensors that provide similar feedback may make affordability a non-issue, but if they simply generate more data, they won’t move the industry any closer to predictive maintenance.

Predicting failure for a specific machine may be a challenge beyond the scope of vibration analyzers and temperature probes. A broad sample is needed for reliable prediction. Equipment manufacturers theoretically could determine the early warning signs of a breakdown, but OEMs seldom are able to benchmark performance once machines leave their shops. That’s beginning to change.

After two years of data collection with a computerized maintenance management system (CMMS), deli-meat processor West Liberty Foods had amassed an impressive database on the performance of 50 high-speed slicing machines. Analyzing that data was another matter, so fault signals and repair data were exported to an Excel spreadsheet and turned over to the slicing machines’ OEM. The vendor was able to identify three maintenance procedures that addressed many downtime events.

Tetra Pak Inc. ( is taking a more expansive approach with a remote monitoring service involving its global installed base of more than 5,000 packaging machines. It begins with beefed up sensing capabilities on machines, augmenting the sensors already on board. Two or three additional sensors are retrofitted onto each servo motor, according to Paul Grainger, technical key account manager at Tetra Pak’s U.S. headquarters in Denton, Texas. With anywhere from two to 20-plus servos on each machine, it isn’t a stretch to call those packaging systems smart machines.

Sensor data are uploaded to a cloud server and analyzed by service experts in London. When heat or vibration from a component begins to move into a certain range, the manufacturer is alerted to the predicted time to breakdown, based on machine failures across the network. If the manufacturer subscribes to Tetra Pak’s plant care solutions service, a technician with a Microsoft HoloLens is dispatched, linking the technician to the London experts and guiding the pre-emptive maintenance.

HoloLens adds a Buck Rogers flair to the service. Field technicians communicate in real time with the London experts through Skype, which also is owned by Microsoft. Grainger downplays the optics, however. “It’s not the hardware investment,” he says. “The value comes from the algorithms we’ve developed, the ability to benchmark against similar equipment and the dedicated personnel’s competence in interpreting the data.”

In a pilot test, a U.S. processor of fluid milk quantified a 48-hour time savings in recovering from a downtime event, thanks to a HoloLens-equipped field technician. With the headgear giving experts a real-time view of the machinery and the tech viewing diagrams and other support materials, root-cause analysis was simplified and accelerated. Otherwise, an “escalation process” of identifying the problem area, matching it to the appropriate expert, and flying that individual to the site would have delayed getting the machine back on line by two days.

The service is limited to Tetra Pak equipment, although it is evolving to “a plant-wide approach” to include robotic machinery and other sophisticated equipment, notes Grainger. The firm is “exploring possibilities” of extending the service to other OEMs’ equipment and technical-service centers.

OEM feedback loops

Schneider Electric USA ( is developing a similar “augmented reality” system using Google glasses, tablets or similar devices, according to Eric Lemaire, food & beverage segment marketing manager. As with the Tetra Pak system, it aims to curtail fishing expeditions and instead target maintenance activities at likely problem areas.

“We’re working with OEMs to help improve basic maintenance of their machines,” says Lemaire. He describes the effort as being in the proof-of-concept stage as Schneider collects data on machine performance and breakdown precursors such as electric draw.

Milwaukee-based Rockwell Automation ( has similar ambitions. By early 2018, it expects to launch a subscription service aimed at OEMs to monitor the health of their machines in the field and improve overall reliability and productivity.

“The cloud capability is a prerequisite for allowing OEMs to participate in the machine-health monitoring process,” says Todd Smith, manager of a suite of analytics products the automation firm is introducing. How a machine is operated can have a big impact on performance, he points out, and part of Rockwell’s focus is “to compare machines to each other to understand what makes those machines act so differently.”

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