While the fourth Industrial revolution has slowly been emerging from AI and the Internet of Things, IoT has already arrived on the factory floor with the force of Kool-Aid Man exploding through walls and IBM making major announcements on the AI front which we lucky to witness at the recently concluded IBM Developer Day.
While Big Data, Analytics and Machine Learning are starting to feel like anonymous business words, but they're not just overused abstract concepts, these buzzwords represent huge changes in much of the technology we deal with in our daily lives. Some of those changes have been for the better, making our interaction with machines and information more natural and more powerful. While others have helped companies tap into consumers' relationships, behaviors, locations and innermost thoughts in powerful and often disturbing ways and the technologies have left a mark on everything from our highways to our homes.
It's no surprise that the concept of "Information about Everything" is being aggressively applied to manufacturing contexts. Just as they have transformed consumer goods into smart, cheap, sensor-laden devices paired with powerful analytics and algorithms they have also have been changing the industrial world as well over the past decade. The "Internet of Things" has arrived on the factory floor with all the force of a giant electronic Kool-Aid Man exploding through a cinder-block wall.
Rightly Tagged by many industry veterans as "Industry 4.0,"the fourth industrial revolution has been unfolding slowly yet rampantly over the past decade with fits and starts largely because of the massive cultural and structural differences between the information technology that fuels the change and the "operational technology" that has been at the heart of industrial automation for decades.
As with other mergers of technology and Artificial Intelligence the potential payoffs of Industry 4.0 are enormous. Companies are seeing more precise, higher quality manufacturing with lowered operational costs, less employee downtime because of predictive maintenance and intelligence in the supply chain. Other industries are also benefiting from having a system of sensors, analytics to process "lakes" of data, and just-in-time responses to emergent issues like aviation, energy, logistics, and many other businesses that rely on reliable, predictable reports which could also get a major boost where we are trying to assist enterprises the best possible way we can via our strategic IT consulting audits.
Sensors and sensibility
The term "Industry 4.0" was coined by Acatech (the German government's academy of engineering sciences) in a 2011 national roadmap for use of embedded systems technology. Intended as a way to describe industrial "digitization," the term was applied to mark the shift away from simple automation with largely stand-alone industrial robots toward networked "cyber-physical systems" information-based orchestration between systems and the humans working with them, based on a variety of sensor and human inputs. Machines that communicate with each other, inform each other about defects in the production process, identify and re-order scarce material inventories. This is the vision behind Industry 4.0 and in the future smart factories using additive manufacturing such as 3D printing through selective laser sintering and other computer-driven manufacturing systems are able to adaptively manufacture parts on demand, direct from digital designs.
Sensors also keep track of needed components and order them based on patterns of demand and other algorithmic decision trees taking "just-in-time" manufacturing to a new level of optimization. Optical sensors and machine-learning-driven systems monitor the quality of components with more consistency and accuracy than potentially tired and bored humans on the product line. Industrial robots work in synchronization with the humans handling more delicate tasks or even replace them entirely. Entire supply chains can pivot with the introduction of new products, changes in consumption, and economic fluctuation. And the machines can tell humans when the machines need to be fixed before they even break or tell people better ways to organize the line all because of artificial intelligence processing the massive amounts of data generated by the manufacturing process.
The Defense Advanced Research Projects Agency (DARPA) has also used research programs such as the Adaptive Vehicle Make project to seed development of advanced, information-integrated manufacturing projects and continues to look at Industry 4.0-enabling technologies such as effective human-machine teaming which is the ability of machines to adapt to and work side by side with humans as partners rather than as tools and smart supply chain systems based on artificial intelligence technology and an effort called LogX. Researchers at MITRE Corporation's Human-Machine Social Systems (HMSS) Lab have also been working on ways to improve how robotic systems interact with humans. As part of that work, MITRE has partnered with several robotics startups including American Robotics, which has developed a fully automated drone system for precision agriculture.
Called as Scout, the system is an autonomous, weather-proofed unit that sits adjacent to fields. All a farmer has to do is program in drone flight times, and the AI handles drone flight planning and managing the flight itself, as well as the collection and processing of imagery and data, uploading everything to the cloud as it goes. It's also a goal that industry has been chasing for a very long time. The concept of computerized maintenance management systems (CMMS) has been around in some form since the 1960s, when early implementations were built around mainframes. But CMMS has almost always been a heavily manual process, relying on maintenance reports and data collected and fed into computers by humans—not capturing the full breadth and depth of sensor data being generated by increasingly instrumented (and expensive) industrial systems. Predictive maintenance systems—such as IBM's Maximo, General Electric's Predix and MATLAB Predictive Maintenance Toolbox—are an attempt to harness machine learning and simulation models to make that level of smartness possible. "Predictive maintenance is the leading application in making use of that data in the field especially in areas where components are really costly, such as wind energy.
There are other approaches to prognostication, some of which bleed into managing the overall operation of the plant itself. IBM's Maximo APM, for example based on IBM's Watson IoT platform builds its baseline from sensors and other data from equipment on the factory floor to continuously refine its algorithms for maintenance. Another Maximo package focuses on overall plant operations, identifying process bottlenecks and other issues that could drive up operation costs.
Bridging the gap between Data and Knowledge
But there are several challenges that companies face in making predictive systems effective. The old computing proverb of "garbage in, garbage out" definitely still applies. MathWorks notes that the main challenge is bridging the gap between the two knowledge domains needed to make predictive maintenance work. Even when there's good collaboration, there's another problem for many predictive models: while there's plenty of data available, most of it is about normal operations rather than failures. In some cases, manufacturers perform "run to fail" tests to collect data about how their equipment acts as components start to push outside of their normal operating parameters.
But their "run to fail" tests involve creating failures, and purposefully breaking costly and complicated manufacturing hardware is uncommon. "You don't want to run a scenario where you break your wind turbine," The last gap to be bridged is how and where to process device data. In some cases, for safety or speed of response, the data from equipment needs to be analyzed very close to the industrial equipment itself even having algorithms run on the embedded processor or procedural logic controller (PLC) that drives the machine. This gets you the best of all worlds.
White paper excerpt analysis by
Kalyan B, Solutions Architect
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