Welcome to this installment of the IBM cheat sheet series: your jargon-busting, straight-talking guide to IoT technologies. Today, we shine a little light on the concept of Intelligent Visual Inspection, and, in particular, the IBM Maximo PQI – Visual Insights solution.
Impress your boss! Intelligent Visual Inspection in ten seconds
Before we delve into the whys and wherefores of Intelligent Visual inspection, here’s an official-sounding definition for you to memorize. Bonus points if you can say it in one breath:
‘Intelligent Visual Inspection is the process of using cognitive capabilities to review and analyze components, products and parts for defects. It works by comparing live assembly line images with previously analyzed and classified pictures of image defects, to identify possible faults. This is particularly valuable when these faults aren’t visible to the naked eye. An intelligent visual inspection tool like the IBM Maximo PQI – Visual Insights solution can reduce inspection times for manufacturers by up to 80 percent.’
Why do we need it?
Once upon a time, manufacturers relied on manual inspection to ensure their products (and parts of products) were functioning correctly, before packaging them up and sending them to market. A human would have to laboriously inspect every component for scratches, missing or misplaced parts, colour errors and other flaws. As you’ve probably anticipated already, there are a few problems with this approach. It is:
Intelligent Visual Inspection was designed to tackle these difficulties. It aids factory inspectors by automating part of the inspection process and augmenting the capabilities of the inspector.
How does it work?
Intelligent Visual Inspection comprises two main tools: images captured using high definition cameras, and IBM Watson’s AI-powered capabilities. Images of normal and abnormal products are fed into Watson’s central learning service to teach it the difference between faulty and correctly functioning components. Watson uses these data to build an analytics library of known defects that can identify quality issues and learn continuously from feedback. This technology can be used to spot defects in live images from the assembly line.
A camera captures images of product components as they move through production and assembly, and feeds these to Watson for analysis. By comparing them with the images it has already processed, Watson can detect defects as minor as tiny scratches and punctures as small as pinholes. It also knows whether parts are in the correct location and are the right shape and color.
Watson’s classification of potential quality issues is automatically sent to manual inspectors, who perform a secondary check and can override the solution’s decision if need be.
It ain’t perfect, but it’s always self-improving
The beauty of a solution like IBM Maximo PQI – Visual Insights is that it’s always learning and improving. When manual inspectors review the tool’s classifications for errors, it takes on their feedback and plugs it in to the next training cycle for that particular analytics model. In short, this is a truly cognitive tool that learns from its mistakes, just like a human does.
With enough data, the solution becomes more accurate, which further augments the capabilities of inspectors. Training time is reduced, inspector turnover becomes less problematic, and overall accuracy increases. Not bad. And because of this self-learning capability, even very small defects can be spotted and reported automatically, saving manufacturers time and money.
It also provides cognitive insights for more efficient problem resolution in the future. This means tackling the root causes of errors to ensure the same conditions aren’t repeated.
Use case for Intelligent Visual Inspection
IBM Maximo PQI – Visual Insights is already bringing benefits to many of its early-adopting clients. An electronics company, for example, is using IBM cognitive technology to automate product inspections to boost production quality and throughput. By speeding up the visual inspection process, Watson IoT technology is helping to restore agility in the production line and remove the bottleneck caused by time-consuming inspections.
A period of just three months saw an inspection lead time reduction of over 85 percent. The combination of AI and human expertise led to improved product quality and faster, more accurate defect detection.
There are plenty of other use cases for this technology, too. The video below demonstrates how IBM Visual Insights works:
Summing up: the advantages
So there you have it: Intelligent Visual Inspection de-mystified. We’ll leave you with a summary of its three main advantages:
Inspection times reduced by up to 80%
Defects reduced by 7 – 10%
Augments the capabilities of human staff and frees them for more specialized work
The total number of Internet of Things (IoT) devices is expected to hit 10 billion by 2020. With this explosion of growth also comes an uptick in the conversations around how to use these devices and the data they generate to improve our businesses and our lives. At IoT World, North America’s largest IoT event ...read more
It’s been an exciting week as more than 12,000 people converged on the Santa Clara Convention Center for Internet of Things World 2019! Topics have spanned from connected cars and smart buildings to Industry 4.0 and developing an IoT Platform. In the full recap going out in the next few days, we will highlight the ...read more
Over the last 20 years, huge investments have been made in metropolitan areas to understand the immense volume of data collected about the citizens inhabiting these cities. Hundreds of millions of dollars have been spent combining data with emerging mobile, sensor and machine learning technologies. This is all done with the goal to improve infrastructure ...read more