Ever since IBM rolled out the world’s first mainframe computer in the 1950s, engineers and manufacturers in information technology (IT) have been pushing the boundaries of possibility through microelectronics and software. However, modern computer capabilities did not surface in the industrial operations technology (OT) space until recently, as machine builders began to realize the benefits IT can provide for efficiency and productivity.
A decade ago, digitalization and advanced analytics in OT environments gave adopters a leg up on their competitors. But today, manufacturers cannot keep up unless they lean on IT advances. These advances address challenges for plants, such as difficulty employing knowledgeable personnel, unexpected equipment failures and lack of operational insights for increasing efficiency.
Though many advances in IT have already found their way into OT deployments, artificial intelligence (AI) carries unrealized potential to aid machine builders in achieving profitability, maintaining efficiency and minimizing downtime. Although many know AI can assist in the production life cycle, it can be difficult determining where to start. This article describes benefits of AI-enabled edge devices by covering three areas where many machine builders can use them to improve their processes — predictive maintenance, quality assurance and robotics.
In manufacturing, AI algorithms examine many iterations of a process, capturing its quantitative properties. Matched with example outcomes of pass and fail, the algorithms begin to correlate properties with their respective outcomes. Over time, AI can define and predict outcomes based on the quantitative properties captured during production more accurately (see Figure 1).
Some industrial AI solutions provide data processing capabilities in cloud-hosted servers, requiring plant sensors to transmit data to the cloud for analysis. Following analysis, cloud servers can send insights back to workstations on the plant floor so plant personnel can take appropriate action. While developments in the Internet of Things (IoT) make direct communication between sensors and the cloud possible, direct connection usually is not advisable due to bandwidth constraints and security concerns. Hundreds of sensors on a plant floor simultaneously broadcasting their data for external consumption can clog up the network, creating bandwidth and latency issues. Even if data flows smoothly in both directions between plant floor and cloud, communication must be encrypted to ensure safe operation and protect-sensitive information. Most modern plants require a PC-based solution to collect, store and interpret historical performance data for optimizing production. If all sensor data moves directly to the cloud, data aggregation takes place there, introducing more data integrity risk than if this task is done closer to the original source. Passing data to the cloud prior to analysis increases the time required between data collection and operational decision making. To address these and other concerns, machine builders are implementing AI-enabled edge deviceson the plant floor to provide data collection, insight generation and operational decision making. They also serve as primary arteries for data transmission between plant sensors and the cloud, where further data processing and deeper analysis can take place, for example to compare performance across a fleet of machines installed worldwide. Data transmission through edge devices enhances security because traffic is encrypted, and only necessary data leaves the confines of the plant. These types of edge devices also speed up line production compared to cloud-based AI systems because they perform operational decision making at the plant itself (see Figure 2). Through AI predictive analysis, these edge devices learn to assess data to make decisions without task-specific programming rules.
