Edge Computing and Predictive Maintenance: Preventing Downtime
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.
What is Edge Computing?
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.
Gartner defines Edge Computing as part of a distributed computing topology where information processing is located close to the edge where things and people produce or consume that information.
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:
- Temperature: Monitoring fluctuations to detect overheating or excessive wear.
- Vibration: Analyzing unusual patterns to identify misalignment or early failure.
- Pressure: Observing shifts in pressure to detect leaks or blockages.
- RPMs (revolutions per minute): Tracking motor speeds to maintain efficiency and catch mechanical issues early.
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:
- FactoryTalk Optix by Rockwell Automation: A powerful edge device designed to collect, contextualize, and analyze data in real-time, integrating seamlessly with cloud systems.
- Ignition Edge by Inductive Automation: Known for its flexibility, Ignition Edge can be deployed across different systems and applications, providing comprehensive data collection and real-time analysis.
- Wonderware Edge by AVEVA: This small panel edge device collects data directly from the plant floor and supports real-time monitoring and remote control.
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.
The Role of MQTT in Edge Computing
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:
- Transfer data reliably: By using a publish/subscribe model, it ensures that real-time data is securely transmitted from the edge to cloud systems, even through limited network connections.
- Reduce latency: With its minimal bandwidth requirements, MQTT helps reduce latency, ensuring that data moves quickly between devices and cloud systems, enabling faster insights.
- Integrate with cloud platforms: Edge devices using MQTT can easily connect to cloud solutions like FactoryTalk Optix or DataMosaix, where data is further contextualized and made accessible to decision-makers.
Connecting Edge Computing to DataOps with DataMosaix
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:
- Compare performance across facilities: Whether monitoring multiple plants globally or comparing production lines within a single facility, DataMosaix contextualizes data in real-time, providing valuable insights.
- Empower decision-makers: Edge computing delivers actionable insights for operators, engineers, and executives, enabling data-driven improvements across the board.
Take Control with Predictive Maintenance and Edge Computing
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.