As predictive maintenance solutions continue growing in popularity, more and more companies in a variety of industries are beginning to realize the value in harnessing machine analytics for condition monitoring. No longer susceptible to costly unplanned downtime and unnecessary repairs, major corporations can now utilize advanced predictive maintenance technologies to remotely assess machine health and identify at-risk areas before they become major issues.
Predictive Maintenance and Big Data
While predictive maintenance continues to advance, so does the of adoption rates in organizations. According to a 2018 survey conducted by the PwC that analyzed the maturity of predictive maintenance solutions in 268 companies, they were able to identify four levels of maturity. With the first tier describing visual maintenance methodology, and levels two and three comprised of real-time condition monitoring, it’s the fourth level that is projected to provide companies with the most value.
Level 4 Predictive Maintenance
The fourth level of predictive maintenance combines the real-time condition monitoring of levels two and three but places a greater emphasis on big data to predict faults and assess asset health. As the most advanced level, only 11% of respondents reported being there currently with 60% responding that they’ve experienced improved uptime as a result.
With another 60% of respondents reporting that they plan to implement level four predictive strategies in their processes in the coming years, let’s examine two industry giants currently making strides in that direction.
Multinational energy corporation Chevron recently launched its plan to implement predictive maintenance solutions in their oil fields and refineries. Made possible by recent advancements in IoT and wireless sensor capability and subsequent access to big data, the company has made plans to outfit thousands of pieces of equipment like heat exchangers and other oil refinery machinery with sensors by 2024. This is a grandiose plan that, if implemented correctly, could save the company millions in unnecessary repairs and machine downtime.
Boeing and Airbus
In the aerospace industry, powerhouse companies like Boeing and Airbus are currently racing to implement more advanced predictive maintenance solutions in the way they assess engine health. Similar to Chevron, advancements in sensor capabilities and technology to analyze big data are the driving forces behind this predictive push. Where regular maintenance was conducted at gates or after flights in hangers and relied heavily on past data, advanced predictive maintenance solutions enable real-time, in-flight condition monitoring. With this, technicians can identify potential risks and make necessary changes before incurring any operational delays, saving airlines substantial profit loss.
Will this be the year that your company employs advanced predictive maintenance solutions to your operations? To learn more about the advantages of harnessing your machine data and maximizing uptime, contact the predictive professionals at GTI Predictive Technology today.
About GTI Predictive Technology
At GTI Predictive Technology, it is our mission to provide the best predictive tools on a single iPad platform with services to monitor nearly any asset. We strive to bring our customers the portability, connectivity, and affordability offered by the latest available technologies. Our product family combines wireless portable and online vibration data collection and analysis, balancing, shaft alignment, thermography, and ultrasound into an affordable and completely scalable solution on one simple to use platform. GTI’s predictive technology apps feature many additions that have come directly from customers. Click here to learn more.