• Bridge monitoring

    Bridges have many dynamic parameters that influence degradation and hence reliability and availability of the object. Although bridges are well designed, the intensity by which bridges are used increases beyond original design. Measurement of actual load and bridge behavior must give more insight as to the status of the bridge condition.

    UVS ERL / EVDL measurements provided a complete perspective on bridge the condition, integrated measurements and modeling from weather, vibration, bending, alignment, tension and temperature.

    The measurement configuration has been applied without obstruction of the existing construction and controls. Initially the measurements are applies wirelessly and powered via solarpanels.

    Due to the measurements a reliable 3x times longer maintenance interval could be determined. Furthermore the relationship between bridge usage and degradation could be determined and appropriate action have been taken.

  • Data technology

    Data becomes increasingly important in every day life and businesses. @ UVS we focus on data collection, processing and analyses especially for Predictive Maintenance. Because there are fundamental differences between operational real-time system control and maintenance measurements we briefly explain.

    Operational

    • Span & Offset detection
    • Adaptive to circumstances
    • Degradation detection
    • Configurable
    • Local readout
    • Abnormal measurements
    • Continuous
    • High reliability
    • High absolute precision

    Maintenance

    • Context
    • Degradation in time
    • Autonomic detection of situations
    • Discrimination in circumstances
    • Predict failure
    • Time relative degradation
    • Interval measurement
    • High (relative) resolution
  • Sewerpump availability & energy saving

    Buying availability and performance by replacing assets on forehand and running redundant systems seems to serve the public at best. However spending public money on assets and maintenance which are not necessary is also hard to justify. Further more an increasing discussion on energy efficiency is current.

    A perfect scenario is to execute maintenance when assets are not used or critical just before they have to perform. This prediction needs to take in account weather forecasts, asset degradation. Because energy usage and asset degradation has a progressive character the predictive models also need to take in account the expected influent and pump capacity so the assets are only used at a nominal capacity.

    OpenICM and Sensor technology can just that, on average we where able to save 30% energy, saved travel and replacement costs and reduced the TCO with more than 25%, the system extended that pump and motor lifespan with over 400%.