Abstract:-Considering the importance of monitoring pipeline systems, this work presents the development of a technique to detect gas leakage in pipelines, based on an acoustic method, and on-line prediction of leak magnitude using artificial neural networks. On-line audible noises generated by leakage were obtained with a microphone installed in a 60 m long pipeline. The sound noises were decomposed into sounds of different frequencies: 1 kHz, 5 kHz and 9 kHz. The dynamics of these noises in time were used as input to … Show more
“…The system enhances learning from previous data, identification, extraction and classification of oil spillage patterns but lacks the capability for timing and automatic monitoring of events that led to the oil spillage on PPP. In Santos et al, [11] prediction of leak magnitude in a gas pipeline is presented using NN and acoustic sensors. The NN and acoustic sensors hybrid produced accurate predictions for high frequency signals of oil leakages but could not give correct predictions for low frequency signals even under occurrences of leakages.…”
Section: Related Workmentioning
confidence: 99%
“…[7] Different approaches have been used to evolve systems that monitor, detect, classify or respond to emergencies resulting from oil spillages and leakages. [8][9][10][11][12][13][14][15] These systems are limited by lack of a systematic way of tracking the time of activities, high probability of false detection and inefficient localization of detected activities due to non inclusion of intelligent tools for explicit timing of operations, pattern recognition and data imprecision handling. Discrete event system specification (DEVS) offers a plausible solution for the specification of timing and localization need of this problem, while adaptive neuro-fuzzy inference system (ANFIS) proffers solution for pattern recognition and data imprecision.…”
The importance of timely detection, classification and response to anomalies on petroleum products pipeline (PPP) have attracted pragmatic researches in recent times. There is need for efficient monitoring and detection of activities on PPP to guide leak detections and remedy decisions. This paper develops an intelligent hybrid system, driven by discrete event system specification (DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on PPP. A dataset comprising 330 records was used for training, validation and testing of the system. Result of sensitivity test shows that inlet pressure, inlet temperature, inlet volume and outlet volume have cumulative significance of 71.72% on flowrate of PPP. Hybrid learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities. ANFIS hybrid learning algorithm with training and testing errors of 0.11980 and 0.010233 yielded a correlation of 0.916 between the computed and the desired output and produced optimal consequent parameters to boost the intelligence of DEVS. A testing error of 0.0303 was observed in the evaluation of DEVS-ANFIS system on 33 test data sample, 32 precise detections were made with one incorrect detection, this gives 96.97% level of confidence in the DEVS-ANFIS model for detection, classification and localization of PPP activities.
“…The system enhances learning from previous data, identification, extraction and classification of oil spillage patterns but lacks the capability for timing and automatic monitoring of events that led to the oil spillage on PPP. In Santos et al, [11] prediction of leak magnitude in a gas pipeline is presented using NN and acoustic sensors. The NN and acoustic sensors hybrid produced accurate predictions for high frequency signals of oil leakages but could not give correct predictions for low frequency signals even under occurrences of leakages.…”
Section: Related Workmentioning
confidence: 99%
“…[7] Different approaches have been used to evolve systems that monitor, detect, classify or respond to emergencies resulting from oil spillages and leakages. [8][9][10][11][12][13][14][15] These systems are limited by lack of a systematic way of tracking the time of activities, high probability of false detection and inefficient localization of detected activities due to non inclusion of intelligent tools for explicit timing of operations, pattern recognition and data imprecision handling. Discrete event system specification (DEVS) offers a plausible solution for the specification of timing and localization need of this problem, while adaptive neuro-fuzzy inference system (ANFIS) proffers solution for pattern recognition and data imprecision.…”
The importance of timely detection, classification and response to anomalies on petroleum products pipeline (PPP) have attracted pragmatic researches in recent times. There is need for efficient monitoring and detection of activities on PPP to guide leak detections and remedy decisions. This paper develops an intelligent hybrid system, driven by discrete event system specification (DEVS) and adaptive neuro-fuzzy inference system (ANFIS) for detection and classification of activities on PPP. A dataset comprising 330 records was used for training, validation and testing of the system. Result of sensitivity test shows that inlet pressure, inlet temperature, inlet volume and outlet volume have cumulative significance of 71.72% on flowrate of PPP. Hybrid learning algorithm was observed to converge faster than the back propagation algorithm in the detection of pipeline activities. ANFIS hybrid learning algorithm with training and testing errors of 0.11980 and 0.010233 yielded a correlation of 0.916 between the computed and the desired output and produced optimal consequent parameters to boost the intelligence of DEVS. A testing error of 0.0303 was observed in the evaluation of DEVS-ANFIS system on 33 test data sample, 32 precise detections were made with one incorrect detection, this gives 96.97% level of confidence in the DEVS-ANFIS model for detection, classification and localization of PPP activities.
“…Oil and natural gas are significant natural resources providing approximately 60% of world energy . These natural resources are mostly transported through a network of the insulated pipes, which is known as an economical way to transport large quantities of oil and gas.…”
Section: Introductionmentioning
confidence: 99%
“…It is worth mentioning that this strategy differs from simulation and statistical points of view. See the study of Molina‐Espinosa et al for more details. The second is the external implementation of hardware‐based implementation, such as sensors with impedance or capacitance changes, fiber optic cables, acoustic sensors, infrared rays for image processing, and video monitoring.The third solution, which combines the first and second methods, for example, acoustic analysis and pressure with mass and volume balance. Recently, the neural network has been given special attention for pipelines, with complex physical behavior . This method has some advantages and disadvantages that are as follows:…”
Section: Introductionmentioning
confidence: 99%
“…This paper, by using of gas flow pattern, proposed a novel neural network‐based fault detection method to detect the leakage in the gas pipeline. Compared with most of the recent works that only considered simulation models, in this paper, the acquired practical data from the real life gas pipeline are gathered and utilized for training a neural network to model the process. Some of the data are used for training set to adjust the neural network weights, and others are used to evaluate the performance of the neural network‐based fault detection system .…”
In this paper, by using of gas flow pattern, a novel neural network-based fault detection method is presented to detect the leakage in the gas pipeline. The pipe is divided into four segments, and each segment is modeled by using input/output pressure of the gas flow. For this purpose, the acquired practical data from the real life gas pipeline are gathered and utilized for training a neural network to model the process. Some of the data are used for training set to adjust the neural network weights, and others are used to evaluate the performance of the neural network-based fault detection system. Gathered practical data from a real life pipeline made sure that the proposed method is prominent and applicable for practical implementations. The model was verified with the data obtained from the test in the actual pipeline and compared with leakage mode. KEYWORDS artificial neural network (ANN), gas pipeline, leakage detection, pattern recognition, wireless sensor network (WSN)
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