*1.2. Literature Review*

Due to different data collection methods, the fault identification problem may be classified into the following two categories: Steady-state methods and transient analysis. Steady-state methods, such as vibration analysis, pulse-echo analysis, and acoustic reflectometry, were developed in previous studies for leak isolation [13–16], blockage detection [17–20], and deterioration determination [21–23]. These methods deliver a large number of results with high precision. However, they are usually developed based on some indispensable customized hardware with a long-term operation, which may lead to high costs [24]. In contrast, the application of the transient-based approach is simple and efficient [25,26]. In transient analysis, a pressure wave with appropriate bandwidth and amplitude is intentionally injected into the system [27]. The faults in the system, such as leakage, blockage, and deterioration can easily affect the head changes in the system when they are compared to those in a steady-state condition. The system responses can be freely obtained through a simple operation. However, this has a big drawback because the pressures created by a transient event may be too high to damage pipelines or even cause catastrophic failure in pipelines.

The heuristic algorithm is capable of searching for global optimal solutions [28]. It is therefore commonly used for detecting leaks in WDNs. Vítkovský et al. [29] combined a genetic algorithm (GA) with inverse transient analysis (ITA) to detect leaks and to calibrate friction factors in water pipelines. A GA was utilized to replace the Levenberg–Marquardt (LM) method used in Reference [30] to minimize the difference between calculated and measured heads. Vítkovský et al. [31] considered the shuffled complex evolution (SCE) algorithm to be an optimization tool in ITA for detecting single and multiple leaks in a pipeline system using laboratory observations with various errors (i.e., data errors, model input errors, and model structure errors). They indicated that a model structure error was the most possible limiting factor in field tests of ITA application. Jung and Karney [32] contrasted the performance of a GA and particle swarm optimization (PSO) in leak detection and friction factor calibration in a developed WDN model. They found that PSO provided faster convergence and produced better results than the GA. Haghighi and Ramos [33] exploited a central force optimization (CFO)-based approach as an inverse problem solver for leak detection in a benchmark leaking pipe network (reported in References [30,31]). The CFO-based approach exhibited excellent accuracy in identifying the friction factor and detecting the leaking node. Covelli et al. [34] highlighted the susceptibility of aged and high-pressure zones in leakage occurrences in WDNs and applied a GA to determine the optimal number, positioning, and setting of pressure reduction valves for reducing background leakages within the network.

Blockage detection is a crucial issue in aged pipelines and pipe networks in energy, chemical, and water industries. A blockage consists of chemical or physical depositions [26] or a valve that has only been partially reopened. It may cause system failures and an increase in water leakage due to the high-pressure redistribution within the system [35]. On the issue of blockage detection development, Wang et al. [10] detected discrete blockages in pipes by analytically using the transient damping of different frequency harmonics. However, detection of the blockage location was not mentioned in their study. Mohapatra et al. [36] developed a technique for detecting partial blockages in a single pipeline using the frequency response method. The patterns and numbers of peaks were used in the pressure frequency response of the system to detect blockage locations and estimate the effective size of two partial blockages. Lee et al. [37] numerically determined the properties of blockage-induced oscillations using the Fourier transform of the inverted peak magnitude in the frequency response diagram. Meniconi et al. [35] investigated two transient-based methods, pressure signal analysis and frequency response analysis, to detect a partial blockage in experimental pipes. The results showed that the former was more accurate in detecting the location of the blockage, while the latter was

more reliable in predicting the severity of the blockage. Duan et al. [38] examined wave–blockage interactions under unsteady flow in pressurized pipelines. They revealed that an extensive blockage might change resonant frequencies and amplitudes, but a partial blockage might only affect resonant amplitudes. Lee et al. [27] used analytical, numerical, and experimental methods to investigate the importance of signal bandwidth in fault detection. They suggested that both low and high bandwidth signals should be considered in a transient-state system. A low bandwidth signal was used to identify the regions of suspected damage, while the fault's location and properties were pinpointed by the high bandwidth signal.

The condition of the pipe wall in pressurized pipelines changes with their age or operating condition. Pipe wall deterioration may be due to corrosion, material erosion, and external pressures with system aging. At present, the transient-based approach is recognized as a potential tool for the noninvasive detection of discrete and distributed deterioration in pressurized pipelines [39]. Many previous studies have investigated deterioration detection technologies for water transmission pipelines. Stephens et al. [40,41] applied fluid transients and ITA to detect changes in the thickness of a pipe wall in a field test. They mentioned that the loss of cement mortar lining could lead to wall corrosion and significant changes in wave speed. Hachem and Schleiss [42] presented a transient-based approach to determine the stiffness of a pipe segment and identify the location of a structurally weak segment of a single pipeline. The location and length of the weak segment were identified using two mean wave speed values and the travel time of the reflections from a weak segment. Gong et al. [43] applied time-domain reflectometry (TDR) analysis to detect distributed deterioration in an experimental water transmission pipeline in a laboratory. They found that the size of the pressure wave reflection from a deteriorated section could be affected by any change in the pipeline impedance of the deteriorated section. Recently, Gong et al. [44] developed a new transient pressure wave generator using controlled electrical sparks. They provided high-frequency waves and improved the incident signal bandwidth. The location and length of thinner wall sections in an experimental pipeline system were then determined through a TDR technique.
