Predictive Maintenance: Physical Models or Data Analytics?

Tinga, T. (Speaker)

    Activity: Talk or presentationOral presentation


    In almost any sector of industry, implementation of smart maintenance can yield
    significant cost reductions or availability improvements. One of the elements of smart
    maintenance is the capability to predict upcoming failures and use that to apply just-intime
    maintenance. This predictive maintenance concept requires on the one hand
    appropriate monitoring strategies to collect information on the loads, usage or
    condition evolution of parts and systems. On the other hand, models and algorithms
    are required to translate this collected data into reliable predictions of the remaining
    useful life. Whereas the monitoring part is becoming more or less mature nowadays,
    the big challenge is in the processing of the data to achieve proper predictions. In this
    presentation, two approaches for this prognostics challenge will be discussed and
    compared. The first approach is based on models describing the physical degradation
    processes and the second approach is based on data analytics. The pros and cons of
    both approaches will be shown and various case studies will be used to demonstrate
    this, ranging from military vehicles and naval ships to wind turbines and helicopters.
    Period3 Feb 2017
    Event titleMaintenance Research Day 2017
    Event typeConference
    LocationUtrecht, Netherlands