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Prashant Kapadia

Innovation

How Edge IoT Platforms Increase Efficiency, Availability, and Productivity

Running quality analytics on the edge enables faster decision making

Published: Wednesday, March 2, 2022 - 13:03

In four years, more than 30 percent of businesses and organizations will include edge computing in their cloud deployments to address bandwidth bottlenecks, reduce latency, and process data for decision support in real time. Edge computing accomplishes this by bringing the businesses’ computational processes closer to the data sources, increasing the speed of these actions.

Additionally, even if a single node is unreachable, the service should still be accessible to users. In this way, edge computing promises to deliver the internet of things (IoT) reliably and quickly while taking more care of security and data privacy. What’s more, 69 percent of organizations say that prioritizing edge-based analytics will improve their ability to meet IoT objectives for specific use cases.

Industries, including manufacturing, water and wastewater, utilities, and building, are implementing hybrid strategies to enable real-time analytics, such as machine-anomaly detection and diagnostics, quality analytics, energy analytics, and overall equipment effectiveness (OEE).

First, anomaly detection on the edge leverages machine learning to monitor machine health, detect anomalous data from sensors, and reduce the time it takes to get critical information. Advanced notice of anomalous machine behavior gives maintenance employees time to prevent breakdowns before they occur, saving the business time, money, and resources.

Additionally, running quality analytics on the edge enables faster decision making, which is important for many industries. This type of data analysis on the edge is important for businesses that use real-time data to improve productivity, require solutions that scale over time, or reside in a fast-paced environment full of unexpected changes. Edge computing gives you access to analytics and actionable insight on the edge, right where the data are generated.

Energy analytics on the edge has allowed utility companies to get real-time data at remote energy-production facilities such as wind turbine farms or solar farms. It is not practical for remote equipment at these locations to quickly transmit data to and from the cloud, slowing the data analytics process. However, if data are quickly processed on edge computing devices, employees have access to real-time data that reflect the current state of energy production.

Lastly, OEE measures how well a manufacturing operation is used compared to its full potential by measuring the percentage of manufacturing time that’s truly productive. This includes measuring the speed at which the parts are produced (performance), the quality of the parts that are being manufactured (quality), and the number of interruptions to the manufacturing process (availability). A perfect score of 100 percent indicates that all the manufactured parts are good, they were produced at maximum speed, and they were produced without interruption. Measuring these aspects is a best practice for any manufacturing operation. Bringing OEE onto the edge allows businesses to measure their key performance indicators (KPI) easily and pivot their business with agility.

Edge-enabled machines provide the data to give you insight and foresight into manufacturing or the utility floor near your asset; you can take preventive corrective action, even when the opportunity to prevent problems is small.

Centralized cloud analytics stumble in critical manufacturing areas

Many enterprises have adopted cloud-first strategies. They have married their workflows to cloud platforms to connect low-cost, elastic global infrastructure with rich device data. Initially, this approach allowed these organizations to accelerate deployment of connected products and industrial internet efforts. However, as enterprises scale their digital transformation efforts, cloud-only approaches limit growth because of delays transmitting data from devices to the cloud, and transmitting analytics from the cloud back to devices. IoT use cases on the manufacturing floor often have unique, real-time data analysis needs. It’s not always practical, economical, or even lawful to move, store, and analyze IoT data on a core cloud infrastructure.

Manufacturing professionals recognize these limitations. They cite security concerns, the high costs of repeatedly accessing data, reduced data accessibility, and the subsequently reduced ability to make real-time decisions as the chief downfalls of analyzing IoT data in the cloud. The solution to these latency issues is to continue to scale businesses using edge computing.

Edge computing solutions, which merge hardware and software into increasingly smaller devices that run smarter analytics onboard, enable real-time decisions and insights. Momentum for edge IoT-solution deployment is increasing at a faster rate in the manufacturing, utility, and building use cases.

Edge computing often incorporates machine learning and artificial intelligence (AI) technologies. These techniques make the calculations performed on the edge even more efficient. That way, the system doesn’t require the help of human operators to identify data irregularities that may point to a potential problem developing with a machine or system. AI can flag anomalies in an actionable way so machine breakdowns can be prevented.

Another use case for AI on the edge is detecting defective parts in a manufacturing operation. This technology can guide part inspectors or identify patterns that may lead to the production of defective parts.

However, the accuracy of the models that AI uses for these purposes may degrade over time. This is where machine learning becomes important. Machine learning is incorporated into the process to create a closed loop in which the computer contains supervisory programming that observes the accuracy of the AI model over time by analyzing data drifts within the AI model.

All these technologies are available through CIMCON’s versatile iEdge 360 Edge Computing Platform. This system is designed to integrate both wireless and wired sensors into the IoT network. The iEdge 360 platform compiles, validates, quality-checks, and processes data. It efficiently uses bandwidth to store and forward data, creating a sensor data lake. The data collected are also used to detect anomalies in machine operation on the edge.


CIMCON Digital iEdge 360 Edge Computing Platform enables multiple use cases

The platform gives users insights into machine operation and process data that would otherwise be unavailable, including automated KPI calculation, derived statistical data, and long-term trend analysis. This gives operators the process visibility they need for situational awareness, energy analytics, and real-time detection of anomalies. It helps you stay on top of your operational goals, efficiency objectives, and machine health status in a simple package that keeps your business running smoothly.

When an anomaly is detected, the iEdge 360 platform provides machine operators with the tools to determine the cause. Drill-down widgets and rule-based alerts couple with machine learning technology to enable easy machine diagnostics. KPI calculations and machine-fault-mode diagnosis take the raw data collected by the system and turn them into actionable intelligence. Rather than allowing you to get lost in the sea of big data, the iEdge 360 platform pinpoints the important nuggets of information and presents them to you in an easy-to-understand manner, enabling operators to quickly fix the issue and get critical processes running again.

Overall, these features reduce operational downtime, repair costs, and labor costs while increasing energy efficiency and production output.

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About The Author

Prashant Kapadia’s picture

Prashant Kapadia

Prashant Kapadia has 24 years of experience in industrial automation, embedded product, industrial software application design, development, manufacturing, and global business development with companies such as GE Digital, Hitachi, and currently CIMCON Digital. He has been part of the digital transformation of many Fortune 500 organizations.