Healthy Prospects Beckon for Condition Monitoring

More data coupled with better analytics are spurring greater use

By Seán Ottewell, Editor at Large

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The push for more-proactive maintenance at plants is getting a big boost from more-extensive condition monitoring. Increasing capabilities and lower costs to capture critical data, as well as advances in data analytics, underpin the growing use and power of such monitoring.

The overall goal of the chemical industry is “no surprises,” notes Doug Child, director, U.S. chemical industry, Siemens Industry, Inc., Buffalo Grove, Ill. “That means less downtime, no unplanned shutdowns and the highest possible availability of the production systems.”

Integrated condition monitoring can play a crucial role in achieving this goal. So, vendors like Siemens have bolstered their portfolios of intelligent field devices with capabilities for self-diagnosis as well as finely tuned instrumentation technology and smart sensors with advanced software algorithms for highly accurate continuous measurements. Coupling such devices with today’s more-powerful control systems, modern communication protocols and advances in wireless technology makes condition monitoring one of the most powerful weapons in the maintenance armory.

Meanwhile, technological advances that are part of the Internet of Things (IoT) are eliminating the data silos that exist today between condition monitoring, computerized maintenance management systems, and other disconnected systems, explains Drew Mackley, director of reliability solutions with Emerson Automation Solutions, Knoxville, Tenn. This promises substantial additional benefits, he says. “With more precise focus on the production assets that require attention, organizations will further improve the overall plant reliability.”

The industry’s conservative nature slows its adoption of innovation, though. “The chemical segment is somewhere in-between slower upstream adopters like oil and gas and faster downstream adopters like pharmaceuticals. However, we’re seeing chemical plants beginning to incorporate more-sophisticated maintenance practices and implementing the technologies to support [them],” notes Jason Hoover, director of digitalization, Siemens Large Drives, New Kensington, Pa.

A Drive For Improvement

As an example, he cites the monitoring of the numerous motors and drives at a typical process facility. “Many plants are beginning to connect these motors and drives to the IoT so that they can send valuable data that enables users to optimize production efficiency and minimize downtime. The evolution of technology, especially around big data, has certainly played a key role.”

Because both sensors and IoT data connectivity and analysis have become exponentially more powerful and cost-effective, the value of connectivity has increased very rapidly.

Hoover uses the electric motor to illustrate this point. Until recently, it only was cost effective to monitor the biggest and most critical assets. Today, for a tiny outlay a plant can install a small box on a motor that provides valuable data around motor vibration, temperature and partial discharge. This makes it cost-effective to not only monitor the biggest motors in the plant but also the low-voltage ones that still can incur high costs if they fail.

Multiple motors can communicate wirelessly to a common hub that securely sends their information to a platform —in the case of Siemens, cloud-based MindSphere IoT — where apps can assess equipment health and performance versus similar assets, and compare a motor’s “digital signature” to the one taken during the factory tests. This strategy can produce many powerful insights into the asset’s health and performance, and allows keeping all information about the asset — performance data, maintenance records, engineering drawings, operations-and-maintenance manuals, etc. — in a single repository so users easily can access the most current information.

On a broader note, having a common, integrated database for engineering, operations and maintenance enables creating a digital twin of the whole plant, Child emphasizes. This can allow recognizing disturbances and faults anywhere at an early stage, permitting maintenance services or repairs to occur before any costly breakages or downtime.

One issue that needs closer consideration is how digitalization of condition monitoring data can improve mean time to restore/repair, says Hoover. He cites the example of a power plant that managed to resolve an unplanned trip about 80% faster using data connectivity. This connectivity allowed a Siemens expert to access event and fault data and to begin diagnosis and troubleshooting immediately.

“The expert helped the plant work through two maintenance issues and get the process back up and running, leading to a real, quantifiable productivity improvement of over $120,000 in just this one instance. The ‘virtual expert’ was able to engage almost immediately after the event, even faster than if he were on site,” Hoover adds.

Child points to another important addition to the condition monitoring arsenal: the ongoing rise of intelligent apps. While Siemens has many, two particularly aim at better maintenance planning. The first allows plants to manage drives, motors and gear units all together; the second uses existing diagnostics and process data for anomaly detection in valves and then makes proposals for predictive and just-in-time maintenance.

“When you add in the connection of mobile devices, paperless order management and remote working, you get a seamless information flow between the management of an asset and the real work onsite. It completes the overall concept of digital services,” adds Child.

Decreasing sensor costs and increasing power of data services will spur even greater connection of plant floor equipment, agree both Child and Hoover. However, digitalization extends beyond the networking of devices and data, or adding digital functionality to existing products, stresses Child: “It is moreover the need to keep the chemical plant and the data state-of-the-art in a digital twin due to ongoing changes — and to use ‘as is’ instead of ‘as built’ data.”

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