Predictive Maintenance Beyond Factory Floor
The term Industry 4.0 originated a decade ago. It spans a wide array of digital transformations but when it comes to predictive maintenance, industry 4.0 often centers around the single industry of manufacturing.
Predictive Maintenance in Manufacturing
The manufacturing process in the US, and across the world, is extremely labor intensive with that labor often performed by heavy machinery. In fact, for most goods, the idea of making something by hand is no longer a possibility. The entire process is reliant on machines. As a result, when machines fail the entire process grinds to a halt.
With the staggering cost of unplanned downtime, manufacturing certainly make sense as a place to start with predictive maintenance. However, it is important to note that it is in fact just a start.
As IoT matures, the conversation needs to shift away from the factory floor to understand how AI-based predictive maintenance, and intelligent monitoring can benefit all industrial equipment no matter what or where it is.
Condition Based vs Predictive Maintenance
As machines become more connected, the first step in making them more intelligent has been condition based monitoring. With condition based monitoring, asset monitoring systems can visualize the current state of equipment, or more accurately the most recent historical state of the asset.
In addition, many systems can establish rules (or conditions) to monitor for. If the condition is met, an event or alert is triggered. The issue, however, is that often times the rules are arbitrary and as the behavior of the machine changes the static rules do not.
The end result, is an event of something that has just occurred.
With AI and predictive maintenance, the AI learns from data generated by the equipment to find patterns and determine the ‘conditions’ on its own. Furthermore, the AI can continue to learn as the machine’s behavior changes.
Predictive maintenance learns what normal operation of equipment looks like to then monitor for abnormal activity indicative of a future problem. In other words, the events or alerts triggered are forward looking, sending an indication of a problem before it occurs.
Intelligent Assets for All
The unfortunate reality is that all industrial equipment has a tendency to fail even if that equipment does not reside in a factory.
Part of the reason for the focus on factory floor machines is that these machines have been connected, sending data to PLCs, for years. Historically, many other asset types have not been digitized, or have been incapable of sharing their digital data.
As IoT has matured, this has changed. Newer generations of industrial equipment and controllers are equipped with smart sensors generating and sharing data that can now be utilized for equipment monitoring.
Whether it’s a water pump in a remote field, a rail switch in a yard, or an HVAC in a building, downtime of assets has significant impact on business and new generation equipment can help change that.
As Smart Environments develop and evolve, the first thing that comes to people’s minds is heating and cooling. If we are truly talking about intelligent assets for all though, a smart environment includes any equipment within that environment.
While most people have viewed predictive maintenance synonymous with factories, in many way factories are just a subset of smart environments.
To simplify the conversation, we can view the makings of a smart environment as being able to intelligently monitor the assets within a building. That could start with the numerous HVAC systems a building contains, but also other utilities such as water pumps, fire alarms, and elevators.
Beyond the core utilities of a building, intelligent monitoring can adapt based on the needs of the environment. Perhaps your environment does consist of heavy machinery on a factory floor but what about a lab where assets such as freezers are critical to your operation? Hospitals require properly running patient monitoring systems, as well as large medical equipment just as MRIs. Maintenance facilities house a collection of industrial equipment used to service other larger types of equipment. All environments contain equipment that is critical to your success. AI, combined with the growing IoT capabilities and data collection, enables all equipment to be intelligently monitored.
Remote Asset Management
What if your environment is external? One primary reason that factory floors were the first use case to take off is that factories are typically contained within four walls and offer a more consolidated space for connectivity.
Remote assets such as water pumps, oil and gas pipelines, and industrial tractors in a field historically lacked that connectivity. In addition, those devices have historically not been digitized. IoT has changed that, not only allowing more and more assets to generate valuable data but to connect that data for analysis.
Downtime of remote assets offer significant challenges mainly due to logistical concerns. An asset in a smart environment typically has someone nearby to assist if the machine runs into trouble. In the event that you do need parts for maintenance, you can send them directly to the same location as the machine and have spare parts and tools on hand.
Remote assets on the other hand tend to be monitored periodically. In addition, all tools and parts need to be brought to the machine. AI-based intelligent monitoring can now transform that to real-time with improved response times and increased efficiencies by having the right parts at the right time.
Because predictive maintenance has become so synonymous with the manufacturing floor, it leaves many to believe it is not for them. Factories are starting to see the tremendous benefits of AI monitoring but the overwhelming majority of industrial equipment deployed globally does not reside within a factory.
Predictive maintenance of remote assets is now a reality and one that opens up a world of possibilities to a larger segment of the industrial equipment market.
As IoT and AI have matured, and continue to do so, we need to expand the conversation of what’s possible.
Predictive Maintenance, or Intelligent Asset Monitoring, has historically been focused on monitoring manufacturing facilities and the critical machines that they contain. As more and more machines become connected through sensors, we can view factories as a subset of smart environments. Smart environments allow us to consider all types of equipment housed within an environment.
Additionally, environments no longer need to be viewed as internal. Advancements in IoT and connectivity such as 5G enable remote asset management in a way that was previously not possible.
Elipsa’s AI templates allow for simple, fast, and flexible asset monitoring. The benefits do not have to remain solely on the factory floor. All industrial equipment can now achieve enhanced logistics, lower maintenance costs, and increased operational readiness.
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