Water main failure prediction

November 2nd, 2016


Exbyte has collaborated with a variety of utilities on failure predictions and related problems, such as critical water main failure prediction, reticulation water main failure prediction, sewer corrosion prediction and active leakage detection. Among the projects, the technology developed by Exbyte will save maintenance cost and benefit 15,000 km long reticulation mains in NSW, reduce uncertainty for a water pipe network of around 9,000 km in Queensland, and enhance the custom experience of over 1.7 million people and over 50,000 businesses in Victoria.


ExByte developed a data-driven solution for predictive maintenance of infrastructures. It is analytics as a service platform that can derive insights from operational data, signal future failure risks, as well as provide decision support for infrastructure owners. It can identify the causal factors of infrastructure failures, prioritize high-risk pipes for renewal and then systematically reduce failure risk over time.

Industry Engagement

Data analysis completed for:

  • 27 national and international water utilities
  • Nearly 9 million pipes
  • 700,000 failure records worldwide


The outcomes of these projects:
Near doubling of consequence cost saving can be achieved based on the case studies.
It is able to recommend high-risk pipes, based on failure probabilities, consequence costs, renewal costs, budget constraints, and geographical constraints.
The improvement of current prediction results to drive better tactical decisions.

Focusing the asset maintenance efforts on preventative repairs has the potential to save the water industry $700 million on reactive repairs and maintenance in Australia.



  • P. Lin, B. Zhang, T. Guo, Y. Wang, F. Chen, “Interaction Point Processes via Infinite Branching Model”, The 13th AAAI Conference on Artificial Intelligence (AAAI), 2016.
  • Y. Wang, B. Li, Y. Wang, and F. Chen, “Metadata Dependent Mondrian Processes”, The 33rd International Conference on Machine Learning (ICML), 2015.
  • P. Lin, B. Zhang, Y. Wang, Z. Li, B. Li, Y. Wang and F. Chen “Data Driven Water Pipe Failure Prediction: A Bayesian Nonparametric Approach”, The 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.
  • B. Li, B. Zhang, Z. Li, Y. Wang, F. Chen, D. Zhang, and D. Vitanage,  “Multi-level Data Analytics for Risk Water Pipe Selection”, IWA Leading-Edge Strategic Asset Management (LESAM), 2015.
  • Li, B., Zhang, B., Li, Z., Wang, Y., Chen, F., Vitanage D. “Prioritising water pipes for condition assessment with data analytics”, Australia’s International Water Conference & Exhibition (OzWater), 2015.
  • Z. Li, B. Zhang, Y. Wang, F. Chen, R. Taib, V. Whiffin, and Y. Wang, “Water pipe condition assessment: A hierarchical beta process approach for sparse incident data”, Machine Learning (ML), 2013.


  • B. Li, Y. Wang, F. Chen, and Y. Wang, “Group Infrastructure Components”, application filed (N14 014-PROVAU), 2015.
  • Z. Li, Y. Wang, and F. Chen, “Bayesian nonparametric method for infrastructure failure prediction”, WO 2014/085849 A1, 2014.
  • B. Zhang, Y. Wang, and F. Chen, “Extended Hawkes process for infrastructure failure prediction”, application filed (N14 012-PROVAU), 2014.
  • B. Zhang, Z. Li, Y. Wang, and F. Chen, “Determining a health condition of a bridge”, application filed (N12 023-PCT), 2012.


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