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Patricia Gómez   Mrs.  Product Manager 
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Patricia Gómez published an article in November 2016.
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Francisco Cubillo

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Canal de Isabel II

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CONFERENCE-ARTICLE 8 Reads 0 Citations <strong>Development of an asset lifetime model for distribution network management</strong> Patricia Gómez-Martinez, Francisco Cubillo Published: 16 November 2016
The 1st International Electronic Conference on Water Sciences, doi: 10.3390/ecws-1-d004
DOI See at publisher website ABS Show/hide abstract

As aging infrastructures require increasing investment for providing a specific level of service to consumers, efficient replacement polices become essential. The key for a better asset management is to set criteria, methods and systems to facilitate that improvement of efficiency at the decision making process while minimizing possible service disturbance events.

 The proposed paper will describe the development of a reliable asset lifetime model as a method for improving replacement efficiency in water supply systems. In this regard, investments will be lead to previously selected elements according to its likelihood of failure and their impact in service provision to the end user. Therefore, the method presents two steps. First of all, a failure prediction model is proposed. In a second step, consequences of failures are measured in terms of service interruption impact.

 As accuracy in failure predictions is increased, renewal investments turn out to be more efficient. Thus, the model has been built through collected data from Madrid distribution network which comprises more than 17.000 km with over 400.000 water mains. The failure prediction model is founded on the statistical analysis of historical network data from over 55.000 system failures gathered during the last four years. Likewise, detailed information from more than 4.400 disturbance events, where data has been carefully recorded through field visits and laboratory essays of soil and pipe materials when failures were repaired, has contributed to its development. The study of such large series of information has allowed not only the identification of intrinsic and dynamic explanatory factors of failures but also the establishment of reliable periods for model’s calibration and validation.

 As replacement priorities change according to system conditions and previous investments, this method will provide a tool for annual forecasting the set of elements that should be renewed to minimize service disturbance.

 

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