Edge Computing in Industry
The industrial sector is a significant factor in the world’s economy. The clothes we wear, the phones and laptops we use, the vehicles we drive and the gas that goes into them all derive from the industrial sector. Industry 4.0, the fourth wave of the industrial revolution, utilizes artificial intelligence to help companies make the most of their data and differentiate themselves in a competitive market.
However, factories incorporating modern, intelligent systems face unique infrastructure challenges. They must have effective mechanisms to capture, store, and process a large amount of data, and do it in real time, or near-real time. For example, the detailed information acquired by intelligent, camera-based product inspection systems can generate a petabyte of data per factory each day. Manufacturers must also ensure the security of their information, Internet of Things (IoT) devices, IoT gateways, manufacturing software, and systems. Vulnerabilities could compromise sensitive data or impede factory processes.
Edge computing in manufacturing addresses these concerns by placing data and secure computing infrastructure closer to the factory floor and the processes depending on it. This approach offers substantial benefits. First, manufacturers can store and process information by avoiding the latency associated with data transfer over long distances. Secondly, the method eliminates the bandwidth costs associated with streaming enormous data volumes to off-site storage.
Edge Computing Challenges
While generating insights through analytics at the edge presents an opportunity for competitive differentiation, turning data into insights involves several considerations:
Generating insights through analytics at the edge — using data generated from products, machines, and operational processes for real-time decisions — presents an opportunity for competitive differentiation. However, turning data into insights comes with a few challenges:
- Industrial facilities often lack the in-house expertise to deploy the needed infrastructure cost-effectively, manage those systems, and connect data effectively.
- Some manufacturers prefer edge solutions offered through cloud service providers. In cases like this, Intel’s Edge Insights for Industrial can complement cloud service providers solutions with supplemental functionality.
- Today’s market offers hundreds of Industrial IoT (IIoT) platforms and thousands of IIoT devices. With that diversity, protocols and operating systems create compatibility challenges between systems. Therefore, end users need open and agile solutions that facilitate compatibility between systems.
Manufacturers need a secure, interoperable, enterprise-ready intelligent edge software solution. They also need the ability to tie all the disparate computing infrastructure, IoT edge devices, and applications together into one end-to-end system.
Intel’s Edge Insights for Industrial
Intel’s Edge Insights for Industrial takes advantage of modern microservices architecture. This approach helps OEMs, device manufacturers, and solution providers integrate data from sensor networks, operational sources, external providers, and industrial systems more rapidly. The modular, product-validated software enables the extraction of machine data at the edge. It also allows that data to be communicated securely across protocols and operating systems, managed cohesively, and analyzed quickly.
Allowing machines to communicate interchangeably across different protocols and operating systems eases the process of data ingestion, analysis, storage, and management. Doing so also helps industrial companies build powerful analytics and machine learning models easily and generate actionable predictive insights at the edge.
Edge computing software deployments occupy a middle layer between the operating system and applications built upon it. Intel’s Edge Insights for Industrial is created and optimized for Intel® architecture-based platforms and validated for underlying operating systems. Its capability supports multiple edge-critical Intel® hardware components like CPUs, FPGAs, accelerators, and Intel® Movidius™ Vision Processing Unit (VPU). Also, its modular architecture offers OEMs, solution providers, and ISVs the flexibility to pick and choose the features and capabilities they wish to include or expand upon for customized solutions. As a result, they can bring solutions to market fast and accelerate customer deployments.
Intel’s Edge Insights for Industrial features a built-in, rapid analytics capability that interprets any amount of data captured from devices located throughout the manufacturing facility.
Additional Benefits of Intel’s Edge Insights for Industrial
Intel’s Edge Insights for Industrial supports many usage scenarios from which software developers can expand their IoT-enabled applications. Developers can also enhance GUIs for simplified accessibility to meaningful information.
Support for a Broad Ecosystem of IoT Devices, Applications, and Tools
Intel’s software supports the management of a wide range of third-party computational, storage, and Industrial IoT device solutions. Intel’s Edge Insights for Industrial is streamlined for industrial applications and uses.
Intel’s Edge Insights for Industrial offers ready-made video ingestion functionality. Intel’s foundation enables metadata capture and creates a relational database to assist machine vision systems in their learning process.
It can also support high-performance inferencing in conjunction with the Intel® Distribution of OpenVINO™ toolkit. With the assistance of the OpenVINO toolkit, machine vision systems can expand their capability using deep learning and neural networks. Doing so enables intelligent insights at the edge. Once computer vision systems train to interpret objects around them, that artificial intelligence can perform repetitive and detailed tasks that prove challenging and tedious for employees.
Machine vision systems provide an excellent quality control mechanism for industrial applications. One manufacturer in China worked closely with Intel to switch its product inspection technique to a machine vision system. The change increased their accuracy in defect detection to 99.9 percent. The new system also performed those inspections much more rapidly than previously possible.
Computer vision can also analyze the industrial processes involved in manufacturing. By identifying procedural bottlenecks, intelligent systems can identify opportunities for greater efficiency on the production floor.
Edge Data Collection and Storage
A facility’s Industrial IoT devices, sensors, and cameras produce large quantities of business-critical information. The first step, of course, is ingesting and processing that data for rapid access by analytics systems and other applications.
Edge networks regularly involve on-site servers for information storage and processing. However, low-latency cloud solutions residing close to the edge can offer a practical alternative in some scenarios. Multiaccess edge computing, for example, lets developers place critical applications closer to their customers, reducing latency and bandwidth costs.
Rapid Insights from Edge Analytics
Intel’s Edge Insights for Industrial features a built-in, rapid analytics capability that interprets any amount of data captured from devices located throughout the manufacturing facility. With minimal latency for data throughput, it enables near-real-time event-driven control. The rapid responsiveness ensures critical information reaches the necessary devices, equipment, and people incredibly fast.
IoT devices and the intelligence behind them regularly serve to monitor mission-critical machinery using predictive maintenance. Should the system interpret unusual activity indicating warning signs of an impending equipment failure, that machine can shut down immediately for service.
Without edge-based analytics systems, the latency caused by processing data at a centralized data center or cloud solution can prove problematic. Edge computing avoids the need for IoT edge data to travel to and from a company’s central data collection system to undergo more in-depth analysis. Even with a fast network or 5G connection, this data movement may take several seconds. By the time the industrial machine’s shutdown order returns to the remote manufacturing facility, it might be too late. That information delay could result in an equipment failure despite all the early warning signs from the edge devices themselves. With local data and edge analytics, though, insights from device data can trigger intelligent actions within milliseconds.
Audi Taps Intel's Industrial Edge Solutions for Multiple Benefits
Intel helps German automaker Audi automate and enhance critical quality control processes in its factories. A scalable, flexible platform solution offers the foundation for current improvements and future innovations, enabling the company to increase efficiency and reduce costs.
With the aid of Intel’s Edge Insights for Industrial, companies of all sizes can integrate their edge devices, data, and assets to optimize their industrial operations. Ultimately, the resulting business insights can lead to many benefits, like rapid decision making, cutting edge security, exceptional predictive maintenance, first-rate operational efficiency, and uncompromised employee safety.