Unplanned downtime at your manufacturing facility isn’t just inconvenient—it’s expensive. Beyond immediate productivity losses, outages can lead to delayed orders, higher operational costs, and strained relationships with customers. But what if manufacturers could predict problems in machinery before they ever occurred—avoiding downtime altogether? This is exactly where edge computing comes into play, providing real-time data and actionable insights directly from the plant floor.
On the most basic level, edge computing involves placing smart devices on-site with your machinery to collect, process, and analyze data as it’s generated, rather than routing it through a PLC and into a SCADA system for later analysis. Edge devices collect information right at the source, allowing plant operators to detect and address issues as they happen.
What do we really mean when we talk about “real-time data” that may give you a head start when it comes to diagnosing your machinery?
Here are a few examples when it comes to key sensor readings:
With this data collected and processed locally, operators receive immediate feedback on equipment health, allowing them to make swift, data-driven decisions. By addressing issues before they cause downtime, predictive maintenance extends equipment life and minimizes costly disruptions.
When it comes to specifics, these are some of the most popular edge-computing devices being used in the market today:
These devices not only capture data but also ensure that it is securely transferred through firewalls and into centralized systems when needed. This makes them essential for driving predictive maintenance strategies across industrial facilities.
One of the key technologies enabling the communication between edge devices and cloud platforms is MQTT (Message Queuing Telemetry Transport). MQTT is a lightweight messaging protocol designed for devices with limited bandwidth, making it ideal for edge computing environments where data needs to be transferred efficiently and securely.
MQTT allows edge devices to:
Edge computing doesn’t operate in isolation—it can integrate with cloud platforms to complement your DataOps strategies to maximize your data’s value. For instance, DataMosaix, a cloud-based platform by Rockwell Automation, powered by Cognite Data Fusion, provides the perfect environment to contextualize data collected from edge devices.
As one Cybertrol engineer noted: “Now, with edge computing, there’s direct connectivity to cloud systems like DataMosaix, where you can send data and give it context. This lets you compare performance across facilities and see why one plant might be running differently from another. And best of all, DataMosaix can decode all of it for you– you don’t need a data scientist to understand what it's telling you.”
By feeding edge data into DataMosaix, manufacturers can:
Edge computing transforms predictive maintenance from a complex task into a practical solution, enabling manufacturers to anticipate equipment issues, reduce downtime, and improve overall efficiency. With Cybertrol Engineering’s expertise in deploying edge devices and cloud platforms like DataMosaix, manufacturers of all sizes can harness real-time data to drive proactive decision-making and maintain smooth operations.