**Chao-Chih Lin and Hund-Der Yeh \***

Institute of Environmental Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan; tom.r1000000@gmail.com

**\*** Correspondence: hdyeh@mail.nctu.edu.tw; Tel.: +886-3-571-2121 (ext. 31910)

Received: 8 April 2019; Accepted: 24 May 2019; Published: 1 June 2019

**Abstract:** This research introduces an inverse transient-based optimization approach to automatically detect potential faults, such as leaks, partial blockages, and distributed deteriorations, within pipelines or a water distribution network (WDN). The optimization approach is named the Pipeline Examination Ordinal Symbiotic Organism Search (PEOS). A modified steady hydraulic model considering the effects of pipe aging within a system is used to determine the steady nodal heads and piping flow rates. After applying a transient excitation, the transient behaviors in the system are analyzed using the method of characteristics (MOC). A preliminary screening mechanism is adopted to sift the initial organisms (solutions) to perform better to reduce most of the unnecessary calculations caused by incorrect solutions within the PEOS framework. Further, a symbiotic organism search (SOS) imitates symbiotic relationship strategies to move organisms toward the current optimal organism and eliminate the worst ones. Two experiments on leak and blockage detection in a single pipeline that have been presented in the literature were used to verify the applicability of the proposed approach. Two hypothetical WDNs, including a small-scale and large-scale system, were considered to validate the efficiency, accuracy, and robustness of the proposed approach. The simulation results indicated that the proposed approach obtained more reliable and efficient optimal results than other algorithms did. We believe the proposed fault detection approach is a promising technique in detecting faults in field applications.

**Keywords:** fault identification; hydraulic transient; inverse transient analysis (ITA); water distribution network; optimization approach
