Neural net versus classical models for the detection and localization of leaks in pipelines

Figures & data

Figure 1. Observer-based leak monitoring.

Figure 2. Neural net.

Figure 3. Estimated leak rate (upper) and estimated leak location (lower) for the PIPESIM model.

Figure 4. Estimated leak rate (upper) and estimated leak location (lower) for the linear distributed-parameter model.

Figure 5. Estimated leak rate (upper) and estimated leak location (lower) for the linear lumped-parameter model.

Figure 6. Estimated leak rate (upper) and estimated leak location (lower) for the neural net model.

Table 1. Standard deviations and the deviations of the mean values of the rate and position errors for all models.

	Value (%) for the following models
	PIPESIM	Linear distributed	Linear lumped	Neural net
Standard deviation, rate	28.4249	8.7816	8.6543	9.3034
Standard deviation, position	9.4026	10.8520	10.8318	4.2284
Deviation of the mean value, rate	27.1487	3.0323	3.2328	3.8741
Deviation of the mean value, position	6.3811	9.6956	9.6815	0.5350