Predictive maintenance is an increasingly popular method of monitoring and managing industrial equipment, predicting when something might fail based on factors such as usage, time since the last service, and the environment in which the equipment operates. Operators then take steps to prevent unplanned downtime by replacing a part before it fails or taking other preemptive measures.

If you haven't already heard, the Internet of Things (IoT) and its subset technologies are disrupting every industry. But what does that mean for manufacturers?

The answer is predictive maintenance.

What is Predictive Maintenance?

Predictive maintenance (PdM)means preventing downtime and equipment breakdowns by identifying risks and starting preventive or corrective action before issues arise. PdM is sensor-based, real-time equipment monitoring and diagnostics. Combined with analytical insights, it helps organizations reduce unplanned downtime and increase equipment uptime.

IoT sensors and artificial intelligence and machine learning algorithms process and analyze sensor data and predict equipment failures. The goal is to improve efficiency by reducing downtime from equipment breakdowns because PdM is proactive instead of reactive. It also changes maintenance schedules based on wear and tear, usage patterns, and other factors.

Predictive maintenance benefits

Companies often use PdM with asset health management (AHM) systems, which help organizations track equipment assets. AHM systems collect data about assets, such as production lines, assets, and equipment, ensuring optimal runtime.

Predictive maintenance:

•Identifies issues before they happen, saving manufacturers a significant amount of money, which makes it one of the most cost-effective ways to increase uptime and reduce unplanned downtime.

•Helps companies optimize maintenance schedules, which means they spend less on repairs and downtime.

•Helps organizations increase revenue by improving production efficiency. With better uptime and less downtime, manufacturers produce more products in less time.

•Helps manufacturers reduce inventory by making accurate demand forecasts.

Use cases of predictive maintenance

Predictive maintenance improves the efficiency of any machinery, including production equipment, transportation equipment, power plants and water facilities.

•Production equipment: Manufacturing production lines are complex, high-value assets that must operate continuously. Sensors often monitor production lines to detect issues such as misalignment, wear, vibration, and corrosion.

•Transportation equipment: Trucks, trains and airplanes are some of the most high-value assets used in logistics — regularly scheduled maintenance keeps fleets in good operating condition.

•Power plants: Electrical grids are complex systems requiring ongoing maintenance. PdM detects issues and schedules maintenance before they lead to service disruption.

•Water facilities: Systems can detect and respond to water conditions, such as leaks and equipment issues before they become costly.

Predictive maintenance challenges

Predictive maintenance helps manufacturers achieve greater uptime by detecting issues before they arise, also helping to optimize maintenance schedules, which means they spend less on repairs and downtime. That's not all, though. PdM also:

•Helps organizations increase revenue by improving production efficiency.

•With better uptime and less downtime, manufacturers produce more products in less time.

•It also helps manufacturers reduce inventory by making accurate demand forecasts.

There are, however, some challenges:

•Data overload: The data generated by sensors can be overwhelming in terms of volume, variety and velocity. It may be difficult to store, analyze, and manage all this data to get actionable insights.

•Geolocation: While sensors collect data, they also need to know where they are. If the sensors don’t know where they are, they won’t be able to provide the correct data, especially true for mobile sensors needing to know their geolocation.

•Standards and integration: Various IT systems must integrate with the sensors to further process the data they generate, making the process complex, expensive and slow.

•Security: Sensors transmit sensitive data, privacy, and security risk. It is also important to keep maintenance information confidential, as it may contain information used against the organization.

To sum it all up, the growing use of IoT and related technologies are creating a variety of new business opportunities and posing new challenges for manufacturers.

Predictive maintenance is helping manufacturers to achieve greater uptime by detecting issues before they arise and helping to optimize maintenance schedules.

With improved uptimes and limited downtimes, manufacturers produce more products in less time, helping manufacturers reduce inventory by making accurate demand forecasts.

David Manney is content strategist with Schuette Metals.