Pipeline Leak Detection and Location Based on Model-Free Isolation of Abnormal Acoustic Signals

Abstract

Pipeline leaks will lead to energy waste, environmental pollution and a threat to human safety. This paper proposes a pipeline leak detection and location method based on the model-free isolation of abnormal (leak and operation) signals. An acoustic signal is first decomposed into “sub-signals” according to its zero-crossing points. Then, based on the definition of signal-to-noise ratio (SNR), the function between the SNR of sub-signal and the number of abnormal sub-signals is established, following which the position of each abnormal sub-signal in the acoustic signal is obtained by tracing its index. Based on this and the cross-correlation analysis, the operation sub-signals can be filtered, which is helpful for the precise leak location. The experimental results demonstrate the computational efficiency and lower false/missing alarm rate of the proposed method that provides an innovative solution for pipeline leak detection.

Control of Pumps of Water Supply Network under Hydraulic and Energy Optimisation Using Artificial Intelligence

“…In the tasks currently solved by the system, the optimisation calculations use the genetic algorithm and fuzzy set logic to define ranges of variable values in quality criteria. At the same time, algorithms based on neural networks are also being developed, including the use of deep learning methods [ 36 ], in particular for the detection and location of hidden leaks on water supply networks, which aims to reduce water losses and, consequently, reduce network operating costs [ 37 , 38 , 39 , 40 , 41 ].…”

Pressure Signal Enhancement of Slowly Increasing Leaks Using Digital Compensator Based on Acoustic Sensor

“…According to the above analysis, the width characteristic of the signal can further constrain the range of the fnormals. Based on the signal decomposition method [27], a compensated signal is decomposed into sub-signals, and the sub-signal width of the maximum peak is taken as its width characteristic which is denoted as WPnormalc(fnormals), as shown in Figure 8. Similarly, the pressure signal is divided into intervals based on extreme points.…”

“…The signal-to-noise ratio (SNR) of the compensated signal is an indicator that can evaluate whether the pressure signal is enhanced. However, the SNR calculation depends on the signal abnormality, which is unknown before leak detection [27]. Therefore, it is not feasible to directly use SNR as an indicator to select an appropriate fnormals.…”

“…The former can be described by the standard deviation, and the latter can be represented by the number of intervals in which the signal can be decomposed by its zero crossings. Hence, this paper proposes the optimization goal shown below:trueprefixmaxJ=cσ+(1−c)M where σ is the standard deviation of the normalized signal; M is the number of intervals [27]; c is the penalty factor and is determined by:c=1N[]ne(i)−ns(i)|maxP[ne(i)]−P[ns(i)] where N is the data length of the signal; ne(i) is the end position of the i th drop interval; and ns(i) is the start position of the i th drop interval.…”

Study on Optimization of Active Control Schemes for Considering Transient Processes in the Case of Pipeline Leakage

“…The disadvantage of this method is that the leakage detection performance is satisfactory only when its magnitude is greater than or equal to 5% of the flow rate under transient conditions. Wang et al [6] 2 of 16 proposed a pipeline leak detection and location method based on the model-free isolation of abnormal (leak and operation) signals, which is helpful in accurately locating the leak location. Using an artificial bee colony (ABC) algorithm, Mandal et al [7] came up with a relatively new leak detection scheme based on rough set theory and support vector machine (SVM) to enhance the accuracy of avoiding false leak detection.…”