The many facets of PdM: Self-driving truck company standardizes, centralizes its maintenance processes
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
- Midstream energy company uses IIoT strategy with integrated CMMS
- Consumer products manufacturer uses AI and ML models
- 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: Torc Robotics, an autonomous self-driving truck pioneer, wanted a customizable CMMS with out-of-the-box features to help address growing operational complexities while maintaining efficiency and reliability. An operational framework was needed to standardize and centralize its maintenance workflows, policies, and procedures for maintaining equipment in its fleet.
Solution: The eMaint X5 CMMS from Fluke Reliability was selected, extensively tailored to the truck company’s needs, and connected to its different systems with eMaint’s application interface (API). Using the CMMS’s features, including a business intelligence (BI) tooling integration and mobile app, Torc standardized its maintenance processes and centralized all asset management.
Results: By improving its workflows, monitoring asset conditions, and addressing issues early with the new solution, the company cut downtime by 50% most months. “Thanks to our API, we can pinpoint patterns of failure and set up triggers that automatically generate a work order, add the assets involved, and provide a duty procedure for technicians to follow,” says Brenton Papenfuse, autonomous service program manager at Torc Robotics. Technicians using the X5 mobile app can report and document asset issues in real time. BI analytics help to identify root causes of downtime.
