The many facets of PdM: Energy company uses IIoT strategy with integrated CMMS to improve machine uptime
The medley of predictive maintenance (PdM) strategies for improving machine health is growing larger and more powerful, whether using classic portable tools for non-critical asset inspection rounds and on-site problem verification and troubleshooting, or advanced technologies such as the IIoT, cloud, and AI and ML algorithms.
Leaders and analysts who go on record by documenting improvements gained from predictive maintenance initiatives provide a window into the immense potential of today’s enabling technologies. This article is one of seven diverse case studies that illustrate some of the many PdM methods and applications employed today.
The other case studies include:
- Analyst foresees AI/ML driving widespread adoption of prescriptive maintenance
- Oil and gas supermajor uses AI predictive analytics
- Consumer products manufacturer uses AI and ML models
- Self-driving truck company uses CMMS, BI tooling, and mobile app
- Tire manufacturer uses 24/7 wireless vibration monitoring system
- Thermal battery manufacturer uses Generative AI-driven data operations platform
- Mining company uses industrial edge data platform and SCADA system
Challenge: Red Cedar Gathering Company gathers, treats, and compresses natural gas from more than 1,200 wells within the Southern Ute Reservation boundary before delivering it to interstate transportation pipelines. A new asset management solution was desired to help automate manual processes, improve machine uptime, streamline data exchange between operations and maintenance, and expedite diagnoses and the mean time to repair (MTTR).
Solution: The Colorado-based company chose to implement CMMS+ asset management software from Llumin and integrate it with their Rockwell Automation PlantPAx control system, data historian, and IIoT sensors. The solution enables predictive and proactive maintenance by monitoring and analyzing historical and real-time data such as temperatures and pressures, and triggering notifications and actions.
Results: With the added insights gained from CMMS+, the energy firm increased its uptime above 99%, resulting in plant throughput of 100% per year. Additionally, its MTTR cycles were significantly reduced. “We require less labor hours due to fewer callouts with equipment going down,” explains Coy Bryant, chief operating officer at Red Cedar Gathering Company. “There is less downtime due to automatic, proactive actions triggered based on equipment condition alerts. We paid for our investment in Llumin in just over two years.”
