How DataOps Powers Predictive Asset Maintenance

How DataOps Powers Predictive Asset Maintenance

Getting your Trinity Audio player ready...

The Industrial Internet of Things (IIoT) has been a game changer for operational maintenance. Real-time, sensor-fed condition monitoring allows manufacturers to shift their asset health strategy from a reactive to proactive approach, so they can prevent failures before they occur. Key reasons to consider a predictive maintenance model include:

• Less unplanned downtime due to unexpected failures.

• Lower maintenance costs because repairs only happen when needed.

• Increased efficiency and productivity for the operations team because they can focus on their core tasks.

• Less labor required for equipment repairs.

Today, manufacturers consider predictive asset maintenance a competitive differentiator, with the market for predictive maintenance technologies expected to grow to $15.9 billion by 2026, a CAGR of 30.6%. But for many manufacturers, the requirements of an effective predictive asset maintenance strategy present some significant barriers.

Common Problems in Predictive Asset Maintenance

To fully leverage the benefits of predictive asset maintenance, data from disparate systems and legacy equipment must be made accessible and usable, but these systems and equipment rarely present their data with any uniformity. Some may produce data that is raw and unstructured, while others produce structured data that lacks the necessary context for the use case. Some of the challenges associated with this overarching problem might include poor integration between historians and analytics, difficulty in identifying which assets are nearing failure, and difficulty in monitoring motor power quality, all vital parts of any predictive asset maintenance strategy.

Correlating and associating historian and analytics streams is often cumbersome. Many manufacturers rely on custom-coded integrations between historians and applications in the cloud, which require substantial maintenance and are not easily adaptable or scalable. Identifying which assets are near failure is made challenging by poor visibility into motor performance data, which can lead to costly line shutdowns and excess scrap. The ability to monitor motor power quality is similarly affected by lack of visibility. Limited performance visibility can impact real-time insights into critical performance variables, such as vibration, temperature, or lubrication analysis, which can impact machine quality and productivity.

These problems all share a common denominator: They can be resolved by access to properly contextualized data. This is where a dedicated DataOps solution shines.

The DataOps Approach

DataOps solutions excel at delivering critical data when, where, and how it is needed. To correlate and associate historian and analytics streams, a DataOps solution will have the data modeling capabilities needed pull high-resolution data from the industrial gateway and the historian, then integrate those streams into a single data model.

To identify assets nearing failure and monitor motor power quality, properly contextualized data must be made easily accessible. A DataOps solution meets this need by first modeling data at the Edge. An Industrial DataOps solution is best deployed on-premises—close to the data’s source—so operators who are most familiar with this data can contextualize, standardize, and model the data before it is streamed to the Cloud. This approach ensures data lands in the Cloud in a ready-to-use format to analyze at scale.

After the data is modeled, it must be collected and published at a consistent, potentially high-frequency rate. A DataOps solution can be configured to publish at any interval from tens of milliseconds to multiple days, ensuring that modeled, contextualized data arrives with the proper frequency.

The Outcomes DataOps Unlocks

A DataOps solution can provide numerous benefits for a predictive asset maintenance program, including faster access to maintenance analytics, reduced dependance on custom coding, and time savings for data science teams who no longer need to curate and cleanse data.

As users iterate and improve their analytical models, they can use a DataOps solution to quickly and easily curate the incoming data set, providing faster analytics insights. A DataOps solution should provide a low-code interface to easily synchronize high-frequency data and publish clean, standardized data models directly to multiple cloud services, significantly reducing custom-coded integrations. This low-code interface ensures scalability by making integrations easily adaptable to fit evolving needs and new equipment onboarding simple.

Finally, with easy access to properly contextualized data, data science teams spend significantly less time curating and cleansing data and more time analyzing the data and building more intelligent analytic models to predict required maintenance.

About the author

This article was written by John Harrington, the Chief Product Officer at HighByte, focused on product management, customer and partner success, and go-to-market strategy. John has a Master of Business Administration from Babson College and a Bachelor of Science in Mechanical Engineering from Worcester Polytechnic Institute.