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IoT video analytics & quality control at the edge: see them in action at HPE Discover

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Steve Fearn,

Chief Technologist, HPE Pointnext

 

Video_analytics_demo.jpgAutomated assembly lines have been a part of modern factories for decades, with machines handling practically every stage of the manufacturing process. However, one step that has remained within the domain of human overseers is quality control. It’s a critical task, but it is also becoming more difficult. As product refresh cycles shrink, components reduce in size, designs are updated more frequently, batch sizes become smaller and customers demand more customizations at the point of manufacture, effective quality control is becoming a huge challenge for progressive manufacturers. 

That is starting to change, though, thanks to the IoT technology revolution sweeping across industry. With new video analytics systems that combine video sensors, machine learning, and high-powered edge processing, manufacturers can speed production, improve quality control, and have human workers concentrate on higher-level tasks. 

Video sensors bring precision and speed 

The concept behind video analytics for quality control is simple. Production works basically as before, using the same legacy equipment, but with video sensors placed at inspection points along the line. The video feeds are sent to nearby edge hardware to be processed. There, the video analytics application compares the video feed with details from the plant’s MES or a bill of materials, and ensures that the finished products match the build list. 

The video analytics system can check for various flaws, ranging from scratches to misaligned parts. It can also spot problems that are particularly difficult for human inspectors to gauge. For instance, at a factory that makes computer servers, the sensors can check the serial numbers of memory DIMMs and dozens of other components that are installed onto a motherboard chassis. Not only can the system verify that the tiny pieces were mounted properly, it can also make sure that the correct components were used. This degree of precision is impractical for high-volume manufacturing using manual QC inspections. 

Even better, the system’s machine learning capabilities mean that accuracy will improve over time. During the initial training phase, ML algorithms may require many thousands of images to determine what can pass the QC protocol. Afterwards, the system can use new data to update its models, leading to lower error rates. 

The edge factor 

Having processing take place right on the factory floor is crucial. If the system has scores of cameras generating thousands of images every hour, uploading everything to the cloud would entail massive bandwidth costs, not to mention latency and other inefficiencies associated with sending data up to the cloud and back down to the plant. Further, no one wants to deal with the risk of disruptions between the plant infrastructure and the cloud -- the whole line would just stop. 

Fortunately, HPE Edgeline system is designed for this type of high-performance computing scenario. The ruggedized devices can host the video analytics application, and process the incoming feeds to compare the images from the production line with reference photos and other data. When you’re looking at multiple production lines with hundreds of cameras as well as other types of industrial IoT sensors, a device like the Edgeline may be the only realistic option to run powerful analytics applications at scale.  

At HPE Discover, there will be many opportunities to learn about video analytics on the edge. Recommended sessions include: 

Integration of Blockchain and video analytics for assembly line quality assurance

Quality assurance (QA) is an important element for manufacturers looking to continuously improve production processes. Complexity of products and the move towards unique customer-defined configurations make traditional human-led methods slow and prone to error. Combine high definition video streaming, edge-based machine learning and object recognition to achieve faster and more accurate QA capability. Coupling this solution with Blockchain, HPE can deliver a fully digital documented record of quality assurance in the manufacturing environment. 

Deep learning video analytics on HPE Edgeline

Artificial intelligence (AI), deep learning (DL) techniques excel at extracting rich metadata from video, so the user can easily focus on attributes of interest. HPE Edgeline Converged Edge Systems and NVIDIA Tesla GPGPUs, with software from our ISV partners, form the perfect platform to do real-time secure video analytics and management at the edge.

Empowering the Digital Enterprise to be more efficient and innovative through data-driven insights from the Internet of Things (IoT)
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