By Eddie Amos, General Manager & VP Industrial Applications Software, GE Digital
What does the future hold for industrial organizations like those in manufacturing? Today’s digital transformation has established the foundation for what’s to come. Like the paved highway systems for cars to drive on, the increased connectivity of industrial assets has been foundational for the next wave of technological advancements.
While the physical machines that make up industrial infrastructure, such as power generators, turbines and locomotives, appear to be relatively unchanged compared to a decade ago, these assets have undergone significant changes to bring them into the digital age. That said, industrials have historically been the slowest to tap into the power of the Industrial Internet of Things (IIoT). According to a recent report
, industrial companies recognize the upsides to IIoT, but 79 percent say they do not have a mature plan in place to reap the benefits of this connected network.
With sensors and intelligent control systems in place across industrial facilities, organizations will now be able to leverage this next wave of technology for more efficient business processes and increased digital maturity. Over the next year and beyond, we can expect a few advancements to take us further than we ever have been before on this digital journey.
Automation through Artificial Intelligence
Plant operators are already seeing the benefits of machine learning that powers artificial intelligence. These software algorithms in asset performance management drive the integration of historical data to help predict maintenance issues and equipment failures with more accuracy and more advanced warning. What will make AI even more valuable is the application of more sophisticated machine learning algorithms to real-time performance data. This will create smarter, more self-sufficient machines. Because many organizations are still in the digital transformation process, connecting all machines and assets across plants and fleets, various enterprise resource planning systems and disparate data hinder data analytics and decision-making. As machines deliver more data through connected sensors, artificial intelligence in asset performance management systems will not only organize and standardize data inputs, but it will also augment an organization’s ability to analyze it in different ways, resulting in more automated and accurate decision-making. Current manual processes, such as issuing work orders and scheduling inspections, will become automated, streamlining maintenance practices and improving machine performance.
Digital Twin Drives New Levels of Productivity
Digital twins are becoming more prevalent across industries, providing a key advantage for organizations that run a true digital ecosystem powered by asset performance management. The digital twin concept is based on the idea of having pre-defined content about how assets are operating and performing, and then leveraging that information to build a replica of the asset. With a virtual replica of every asset that informs operators about health status, equipment life and performance levels, organizations are fully equipped to optimize machines at all hours of the day to adjust for peak demand levels and optimize productivity as a result. For companies that operate active assets every second of the year, this saves millions of dollars by avoiding unplanned downtime.
Once organizations have a better understanding of their entire plant and fleet operations based on the digital replicas of their assets, they can identify any anomalies or poor performing equipment and take action as appropriate. Taking this one step further, this data can eventually be anonymized and shared industry-wide for a larger data sample and more accurate benchmarks. The power of statistics is limited for organizations running a set number of assets, but leveraging a broader dataset will provide better performance insights for asset management.
Not all industrial equipment is easily accessible. Some machines have thousands of parts, each embedded inside layers of the strongest materials, and other machines that operate within the world’s harshest environment. For decades, this has created time consuming, laborious inspection processes for routine checks or when an asset indicated an error. Inspection teams need to perform hands-on testing of the equipment or literally look “under the hood” to identify the problem, particularly for assets that are necessary for operations.
A medical technology manufacturer, for example, has to prioritize inspection for its critical healthcare systems. If there is an infection in a hospital, equipment used to test for infection is critical for immediate response. If a manufacturer’s microbiology machine has any performance issues, it would put the entire healthcare ecosystem at risk. This equipment requires regular monitoring and inspection.
Fortunately, technology is rapidly changing how inspection teams approach these processes. With advances in augmented reality for mobile devices and the help of digital twins, field workers can now perform in-depth inspections based on real-time information without having to take the equipment offline and physically analyze things. As more field service professionals are able to take advantage of augmented reality technology, organizations will minimize the risk that extreme environments can pose to workers - for instance, near pipelines or on oil rigs - as well as maximize efficiency by keeping equipment running online.
These technologies - artificial intelligence, digital twins and augmented reality - are available today, but they will only continue to advance and will become fundamental components of industrial operations. The key to taking full advantage of these newer capabilities is to have the right digital infrastructure in place today, including connected assets that feed information through a centralized asset management system. As more equipment enables data analytics and drives intelligent asset strategies, industrial organizations will have the level of digital maturity they need to optimize asset performance in real-time.