Remote condition monitoring (CM) — the practice of using sensors and software to monitor performance abnormalities in assets — is emerging as a business-critical activity in the food processing industry. The CM concept has been around for a while within other manufacturing arenas. However, new advances in sensor technology that incorporate cloud-based data delivery are making the process more cost-efficient, accessible and effective. CM can be of great benefit to the food processing industry, in which machine disruption is expensive in terms of asset downtime, labor and parts, and product contamination risk resulting from “opening up” machines for sensor installation or repairs mid-cycle.
Remote CM is routinely performed in many industrial manufacturing sectors, ranging from transportation manufacturing to plant environments where motors, piping and heat exchangers are common tools of the trade. In those and other manufacturing settings, CM is considered an improvement upon preventive maintenance, where maintenance activities are carried out according to a schedule based on data about asset longevity or original equipment manufacturer recommendations rather than actual evidence that an asset may need attention. CM allows for predictive maintenance, providing real-time trend data that show fluctuations in asset behavior and sends alerts to facility leaders when those fluctuations have exceeded accepted, user-defined norms for that asset. This approach eliminates the waste associated with unnecessary preventive repairs, while alerting maintenance personnel to problems before they become acute; thus allowing for phased, scheduled maintenance tailored to documented need.
In the food processing industry, externally attached wireless CM sensors can continuously record and display machine activity in real time via cloud-based software and apps. Maintenance personnel on- or offsite can use desktop computers or mobile devices to access data displays and track variances in asset performance and adjust plans dynamically as needed rather than reactively.
Food processing sensors include the following types:
In the absence of continuous remote CM technologies, many food processing environments have taken a run-to-failure approach to their equipment. Given that unscientific (albeit, schedulable) preventive maintenance can be costly and lead to wasteful spending on the premature replacement of working parts, in many processing environments, maintenance staffs have opted instead to let equipment run to failure. While the run-to-failure approach averts unnecessary preventive maintenance, companies pay in other ways: Each time a machine fails without warning, it costs anywhere from hundreds to tens of thousands of dollars per hour in lost productivity1.
Initial machine monitoring solutions for the food processing industry required complex and time-consuming installation protocols, causing engineering crews to run cables or mount detailed bracket-and-screw systems. Some systems required internal installation — a messy process requiring opening up a machine, completing installation and extensive cleanup, all of which increased threats to food products’ risk of contamination from exposure to the outside environment. Further, after all this labor, many of these sensors didn’t provide accurate or easily accessible information about machine performance, and most didn’t provide round-the-clock insight into a system’s operation trends and health. It’s not surprising that against this backdrop many maintenance and factory managers opted for a run-to-failure approach.
Fortunately, modern sensor systems for the food processing market are much easier to launch and provide more transparent and accessible data. Newer sensors are mounted externally to machines — rather than internally — and installation takes less than an hour. Once installation is complete, data becomes accessible immediately via desktop machines or mobile devices. Built from advanced cloud-based technology, the sensors send information continuously to maintenance teams. Every team member can see all data seamlessly in real time, so there’s time to discuss and assess trends in machine performance and determine how to further assess the machine’s behavior and draw conclusions about when and how to address early-stage abnormalities based on measurement.
A single mobile app and software environment allows facility managers to position wireless sensors on or near assets they are measuring, and then link sensors to a secured cloud that is accessible via a desktop computer or mobile device app. Facility managers and maintenance teams can use one or multiple types of condition monitoring sensors within the same system, making expansion of a monitoring program across multiple machine and sensor types easy.
Wireless sensors can monitor several machine behaviors, including vibration levels, multiple aspects of a machine’s power (voltage, current, etc.), temperature (using thermal imaging and/or temperature sensors) or temperature in a controlled refrigerator or freezer setting. Each sensor can be set up to send push notifications as alarms when monitored assets deviate from user-set parameters. Because alarm setup is customizable and push-based, facility operators can customize alarm triggers around a particular piece of equipment’s specifications and also send alerts to select teams or individuals. Wireless sensors paired with CM software also aggregates trend data, stores records and provides information visually in its reports, making facility audits easy and equipment performance metrics history readily available for new maintenance team members as they grow into their roles.
Food processors using the CM software and sensors can benefit from a fast and non-invasive setup protocol, build an informative performance history and make more strategic maintenance decisions. Personnel can track an asset’s performance (whether it falls below or lasts beyond normal expected specifications), which can then inform future equipment acquisition plans. Data from the system can influence strategic maintenance planning, while also minimizing asset downtime.
Lamonica’s Pizza Dough in Los Angeles, California, which manufactures ready-to-bake raw pizza dough for consumers, started testing a Fluke Connect gateway and sensor to remotely monitor its refrigeration unit motors.
“Lamonica’s Pizza tries to provide the best quality dough for a delicious price,” said Jorge Leon, refrigeration mechanic. “Before, what we would do is wait until it breaks; then we’d fix it. Once we had Fluke Connect, we were able to detect problems ahead of time. It became preventive maintenance, more than just repair.”
Because Lamonica’s Pizza Dough offers raw pizza dough, refrigeration units need to operate properly to uphold product quality. Concerns about potential refrigeration irregularities led Leon and Lamonica’s to Fluke Connect.
“We deal with a lot of motors, and it’s good to detect when they’re over-amping,” Leon said, noting that Fluke sensors and the Fluke Connect system logs power currents. “You know exactly what time you were over-amping the motor.”
Historically, Lamonica’s has used many sources for machine data — outside vibration specialists, costly tear-downs and more. However, maintenance teams can get this information from a much more cost-effective and easily-accessed system in Fluke Connect.
Leon hopes to start formally working with a Fluke 3540 FC Three-Phase Power Monitor to better monitor the refrigeration units, especially with the experience he’s already had with the gateway and sensor.
“It proved to minimize downtime by catching the problem ahead of time,” Leon said. “It has eased my mind because we have alarms set up.”
When it comes to food processing, identifying and addressing small manufacturing issues before they escalate into large-scale problems is smart business. Wireless sensors equipped with remote condition monitoring software makes it easy to use today’s advanced cloud technologies to run a more efficient continuous monitoring system.
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 Martinez, Henry. “How Much Does Downtime Really Cost?” Information Management, 29 July 2009, www.information-management.com/news/how-much-does-downtime-really-cost?regconf=1