Six ways edge computing can be leveraged in machine learning applications

Discover the power of real-time analytics, predictive maintenance, and Meta Loop Processing in the age of Industry 4.0

Machine learning applications often utilize edge computing, which means running machine learning algorithms and models on local devices (the “edge” of the network) rather than relying solely on centralized control systems, data historians and cloud servers. 

This approach offers several advantages:

  • Reduced latency: Edge computing minimizes delays in analyzing machine states and processing data, leading to tighter control, faster insights, and improved decision making.
  • Optimized bandwidth: By processing data locally, edge devices reduce the need to transmit large amounts of raw data to remove servers and legacy centralized control systems.
  • Enhanced security: Edge computing improves security by processing sensitive information locally, reducing the risk of data breaches. 
  • Data privacy: With edge computing, organizations can reduce information leaks and maintain data privacy by keeping sensitive information within their local network.
  • Operation in low or no connectivity scenarios: Edge devices can operate effectively in environments with limited or no internet connectivity, ensuring continuous functionality and reliable control.
  • Operation in low or no power scenarios: Edge computing with integrated sensor inputs and control outputs (IoT/IIoT) enables smart devices to function in power-constrained situations, providing reliable control in challenging conditions.

Here are some common machine learning applications that leverage edge computing:

1. Real-time analytics and insights

Edge devices can process data locally and provide analytics and insights in real time. For example, in manufacturing, sensors on the factory floor can analyze equipment performance data, enabling immediate annunciation of conditions and status. This empowers decision makers and helps operators take proactive measures.

“From real-time analytics to predictive maintenance and autonomous systems, edge computing and machine learning are driving innovation and efficiency.”

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2. Predictive maintenance

Machine learning models running at the edge can analyze sensor data to predict when machinery or equipment is likely to fail. By monitoring ware items and critical components, organizations can schedule maintenance before a failure occurs—minimizing downtime, reducing material overhead, and optimizing operational efficiency. 

3. Anomaly detection

Machine learning algorithms at the edge can even help identify potential threats or security breaches. By detecting anomalies in data patterns, such as unusual behaviors in sensor data, IIoT devices, and operator adjustments, manufacturers can develop proactive responses and limit the consequences of out-of-tolerance conditions during the manufacturing process.

4. Image and video processing

Edge devices excel at processing images and videos locally, making them ideal for applications like product recognition, object verification, sortation, analytic inspections, counting, and dimensional analysis.

Smart sensors with edge processing enable advanced modeling of targeted applications and reduce the need to transmit large amounts of raw data to remote GPU processors or the cloud, saving time and bandwidth.

5. Meta Loop processing (MLP)

Meta Loops is a term I coined for targeted process tasks and IIoT-deployed adaptive process control loops.

Challenging multivariable control loops and processes subject to instability greatly benefit from adaptive process control models. With edge processing and IIoT, Meta Loops provide stable control of dynamic and challenging processes. Meta Loop processing utilizes machine learning and artificial intelligence in analyzing and closing control loops closer to the process—at the edge—while maintaining existing integrations and control system feedback and connectivity.

Common stand-alone, single-loop controller applications benefit from Meta Loop processing by adapting to changing process conditions in real-time, limiting operator interactions and eliminating manual trim adjustments.

6. Autonomous Vehicles

Edge computing plays a crucial role in autonomous vehicles. By enabling local sensor data processing, edge computing facilitates tasks like object detection, path planning, and real-time decision-making. This not only enhances safety but also provides a more comfortable and entertaining passenger experience.

Do you have a challenge or process you think could be solved by edge computing? Would you appreciate adaptive cruise control and collision avoidance applied in your process?

From real-time analytics to predictive maintenance and autonomous systems, edge computing and machine learning are driving innovation and efficiency, unlocking capacity, and improving quality across many industries. Reach out to our team today to learn more.

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