Data science and telematics are making it w possible to use a vehicle’s health to predict when PM services are due. This session will discuss the emerging science behind predictive maintenance and how it is being used to set PM schedules. Traditional PM intervals have been based on time and usage intervals that have been in place for decades in some fleets. This can lead to over-service as intervals occur too soon before a failure is likely to happen or unplanned breakdown as failure happens before the service is scheduled. Sensors and diagstic codes send data via telematics that data scientists can analyze to predict the likelihood of failure. This probability can be used as an additional threshold for determining the need for a PM that is more closely aligned with preventing a failure, the goal of a good PM program. The session will discuss the approach to managing PMs using telematics, along with some case study examples of a recent pilot program conducted across several public sector fleets. We will also discuss the role of telematics in monitoring Battery Electric Vehicles (BEV) health and PM scheduling. After this session, participants will be able to:
• Describe predictive maintenance (PM) and compare and contrast predictive, preventative, and corrective maintenance strategies.
• Describe how telematics and diagstics can be monitored to estimate the probability of failure and how this can be used to forecast and prioritize PM services.
• Identify the costs of implementing a predictive maintenance strategy and how return on investment (ROI) is realized.

Contributor/Source

Mr. Marc Knight

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