Unplanned downtime is the ultimate nemesis for facility, operations, and production managers. Between the lost man-hours, suspended production, and cost of repairs, it is enough to ruin your day or worse, throw your budget off track. To avoid these dreaded situations, most manufacturers deploy preventative maintenance practices on a regular basis – but that isn’t always enough. Traditional preventative maintenance resolves issues on only 18% of machinery where it is performed, which means 82% of machines break down due to random or unknown factors. 

Preventative maintenance in an industrial facility
Traditional preventative maintenance resolves issues on only 18% of machinery where it is performed, which means 82% of machines break down due to random or unknown factors.

Those unknown factors can be identified through the consistent monitoring of machinery, and addressed with predictive maintenance. More often than not, manufacturers send employee(s) to roam the facility and manually record the data from various meters and sensors – but this practice can interfere with productivity and allow for human error in transcribing data. Even under the best circumstances, the accurate collection, aggregation, and analysis of that data is a tall order. 

With the Industrial IoT (IIoT), a network of devices with wireless sensors that automatically capture data and then transmit it to a centralized software platform, facility or production managers gain insight into machine performance and where there might be issues. 

Let’s take a closer look at two ways the IIoT can facilitate both preventative and predictive maintenance, to the benefit of your business.

Preventative Maintenance

Through the IIoT, data is collected 24 hours a day and stored in a centralized location (often the cloud) which helps to remove guesswork and estimations around preventative maintenance. Over time, facility managers can meter or collect data to establish benchmarks for regular machine usage and performance, and schedule preventative maintenance sessions at the right time.

Example: In a beverage manufacturing plant, the conveyor belt that transports product to the quality assurance station has a history of unplanned downtime. With operational data tracked 24 hours a day, the conveyor belt’s average total use time is established which helps facility managers understand what the threshold is for preventative or scheduled maintenance. Over several months, data is collected and shows the conveyor belt operates for about 16 hours a day. The facility manager knows at 2000 hours of use, the machine has problems and can breakdown so preventative maintenance is scheduled at 1700 hours to limit the risk of unplanned downtime. 

Predictive Maintenance

With the IIoT, facility managers can more easily (and accurately) get ahead of issues that lead to unplanned downtime. Many times the motors within the machines themselves are early indicators for when there is a risk for unplanned downtime. Through the instrumentation of specialized, IIoT-compatible sensors for vibration and temperature, facility managers can better understand a motor’s status and when predictive maintenance should be deployed. 

Preventative or predictive maintenance being done on a lathe machine
Many times the motors within the machines themselves are early indicators for when there is a risk for unplanned downtime.

Example: A vibration sensor has been affixed to the motors that power a conveyor belt in the beverage manufacturing plant. By carefully monitoring vibration frequency and patterns, the facility manager can understand the baseline for motor’s status and ultimately the conveyor belt status. For a couple of months, the motor’s vibration levels remain in line with the predetermined benchmark so, no maintenance was necessary. However, eventually the vibration frequency and amplitude started to increase. Because of a part that had worn thin, the motor’s vibration increased. The rise in motor vibration was an indication that a part within the motor had worn thin, and needed to be replaced soon. By having the data to show the change in vibration patterns, the facility manager was thus able to predict imminent damage to the components of the motor, and schedule maintenance before it caused any damage to the rest of the mechanism, which could have caused unplanned downtime, and had additional costs.

Whenever a manufacturing production comes to an unexpected stop, the result is a loss of product and productivity, increased stress, and thoughts about how to avoid this situation in the future. The IIoT provides an unprecedented view into machine usage, performance, and health so you can schedule maintenance and stay ahead of unplanned downtime without an impact on productivity. Because you know when and where the risks are, you can shift your focus to other projects without the threat of unplanned downtime. 

Digital Lumens’ SiteWorx Sense, a facility and process monitoring application enables metering and monitoring of energy and other critical utilities, as well as alert functionality that triggers email or SMS notifications when usage data strays from a pre-identified range.

Learn how SiteWorx and the IIoT create new efficiencies and value for your industrial facility:

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