scholarly journals Classification Accuracy Enhancement Based Machine Learning Models and Transform Analysis

2021 ◽  
Vol 23 (2) ◽  
pp. C44-C53
Author(s):  
Hanan A. R. Akkar ◽  
Wael A. H. Hadi ◽  
Ibraheem H. Al-Dosari ◽  
Saadi M. Saadi ◽  
Aseel Ismael Ali
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Leak Detection ◽  
The Novel ◽  
Denoising Method ◽  
System A ◽  
New Novel ◽  
Water Pipeline

The problem of leak detection in water pipeline network can be solved by utilizing a wireless sensor network based an intelligent algorithm. A new novel denoising process is proposed in this work. A comparison study is established to evaluate the novel denoising method using many performance indices. Hardyrectified thresholding with universal threshold selection rule shows the best obtained results among the utilized thresholding methods in the work with Enhanced signal to noise ratio (SNR) = 10.38 and normalized mean squared error (NMSE) = 0.1344. Machine learning methods are used to create models that simulate a pipeline leak detection system. A combined feature vector is utilized using wavelet and statistical factors to improve the proposed system performance.

Catenary image denoising method using lifting wavelet-based contourlet transform with cycle shift-invariance

2019 ◽  
Vol 17 (01) ◽  
pp. 1850060 ◽  
Author(s):  
Changdong Wu ◽  
Hua Jiang
Keyword(s):  
Image Denoising ◽  
Mean Squared Error ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Contourlet Transform ◽  
Signal To Noise ◽  
Denoising Method ◽  
Shift Invariance ◽  
Lifting Wavelet ◽  
Noise Ratio

In the catenary status detection system based on the image processing, quality of the captured catenary image is critical. In order to obtain a high quality image for further analysis, this paper proposes a new catenary image denoising method based on lifting wavelet-based contourlet transform with cycle shift-invariance (LWBCTCS). In this method, the lifting wavelet is first constructed based on wavelet transform (WT). Then, to decrease the redundancy of contourlet transform (CT), the lifting wavelet-based contourlet transform (LWBCT) is built by using the lifting wavelet to replace the Laplacian pyramid (LP) transform of CT. Finally, the LWBCT with the cycle shift-invariance (LWBCTCS) algorithm is combined to reduce the pseudo-Gibbs phenomena of LWBCT. The proposed method not only has the virtues of multi-scale and multi-direction, but also reduces the visual artifacts in the denoised image. The results of comparative experiments with captured catenary image show that the proposed method can achieve satisfactory denoising performance, in particular, for catenary image with abundant texture and detail outline information. It not only eliminates noise but also preserves the textures and details simultaneously. Besides, comprehensive consideration of the denoising performance shows that the proposed algorithm in terms of the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and mean squared error (MSE) is stable than those conventional denoising algorithms, including WT, CT, curvelet transform (CV) and BLS-GSM methods. The visual quality as well as quantitative metrics is superior than those conventional denoising methods.

An In-Pipe Leak Detection Sensor: Sensing Capabilities and Evaluation

2011 ◽  
Author(s):  
Dimitris M. Chatzigeorgiou ◽  
Atia E. Khalifa ◽  
Kamal Youcef-Toumi ◽  
Rached Ben-Mansour
Keyword(s):  
Water Supply ◽  
Environmental Health ◽  
Fresh Water ◽  
Detection System ◽  
Acoustic Emissions ◽  
Leak Detection ◽  
Acoustic Signals ◽  
Clear Understanding ◽  
Water Pipes ◽  
Water Pipeline

In most cases the deleterious effects associated with the occurrence of leak may present serious problems and therefore leaks must be quickly detected, located and repaired. The problem of leakage becomes even more serious when it is concerned with the vital supply of fresh water to the community. In addition to waste of resources, contaminants may infiltrate into the water supply. The possibility of environmental health disasters due to delay in detection of water pipeline leaks has spurred research into the development of methods for pipeline leak and contamination detection. Leaks in water pipes create acoustic emissions, which can be sensed to identify and localize leaks. Leak noise correlators and listening devices have been reported in the literature as successful approaches to leak detection but they have practical limitations in terms of cost, sensitivity, reliability and scalability. To overcome those limitations the development of an in-pipe traveling leak detection system is proposed. The development of such a system requires a clear understanding of acoustic signals generated from leaks and the study of the variation of those signals with different pipe loading conditions, leak sizes and surrounding media. This paper discusses those signals and evaluates the merits of an in-pipe-floating sensor.

Pipeline Rupture Detection Based on Machine Learning and Pattern Recognition

2016 ◽  
Author(s):  
Martin Di Blasi ◽  
Zhan Li
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Task Force ◽  
Detection System ◽  
Leak Detection ◽  
Guidance Document ◽  
Performance Expectations ◽  
Detection Systems ◽  
Short Period ◽  
Recognition And Response

Pipeline ruptures have the potential to cause significant economic and environmental impact in a short period of time, therefore it is critical for pipeline operators to be able to promptly detect and respond to them. Public stakeholder expectations are high and an evolving expectation is that the response to such events be automated by initiating an automatic pipeline shutdown upon receipt of rupture alarm. These types of performance expectations are challenging to achieve with conventional, model-based, leak-detection systems (i.e. CPM–RTTMs) as the reliability measured in terms of the false alarm rate is typically too low. The company has actively participated on a pipeline-industry task force chaired by the API Cybernetics committee, focused on the development of best practices in the area of Rupture Recognition and Response. After API’s release of the first version of a Rupture Recognition and Response guidance document in 2014, the company has initiated development of its own internal Rupture Recognition Program (RRP). The RRP considers several rupture recognition approaches simultaneously, ranging from improvements to existing CPM leak detection to the development of new SCADA based rupture detection system (RDS). This paper will provide an overview of a specific approach to rupture detection based on the use of machine learning and pattern recognition techniques applied to SCADA data.

Oil and Gas Offshore Pipeline Leak Detection System: A Feasibility Study

2014 ◽  
Vol 699 ◽  
pp. 891-896 ◽  
Author(s):  
Mohamad Fani Sulaima ◽  
F. Abdullah ◽  
Wan Mohd Bukhari ◽  
Fara Ashikin Ali ◽  
M.N.M. Nasir ◽  
...  
Keyword(s):  
Evaluation Method ◽  
Oil And Gas ◽  
Detection System ◽  
Leak Detection ◽  
Inspection Time ◽  
Related Data ◽  
Detection Systems ◽  
System A ◽  
Detection Technology ◽  
Offshore Pipeline

Pipelines leaks normally begin at poor joints, corrosions and cracks, and slowly progress to a major leakage. Accidents, terror, sabotage, or theft are some of human factor of pipeline leak. The primary purpose of Pipeline leak detection systems (PLDS) is to assist pipeline operators in detecting and locating leaks earlier. PLDS systems provide an alarm and display other related data to the pipeline operators for their decision-making. It is also beneficial because PLDS can enhance their productivity by reduced downtime and inspection time. PLDS can be divided into internally based or computational modeling PLDS Systems and external hardware based PLDS. The purpose of this paper is to study the various types of leak detection systems based on internally systemtodefine a set of key criteria for evaluating the characteristics of this system and provide an evaluation method of leak detection technology as a guideline of choosing the appropriate system.

Near Real-Time Automated Detection of Small Hazardous Liquid Pipeline Leaks Using Remote Optical Sensing and Machine Learning

2016 ◽  
Author(s):  
Maria S. Araujo ◽  
Shane P. Siebenaler ◽  
Edmond M. Dupont ◽  
Samantha G. Blaisdell ◽  
Daniel S. Davila
Keyword(s):  
Machine Learning ◽  
Crude Oil ◽  
Real Time ◽  
Optical Sensors ◽  
Environmental Conditions ◽  
Detection System ◽  
Leak Detection ◽  
False Alarms ◽  
Different Types ◽  
Pump Stations

The prevailing leak detection systems used today on hazardous liquid pipelines (computational pipeline monitoring) do not have the required sensitivities to detect small leaks smaller than 1% of the nominal flow rate. False alarms of any leak detection system are a major industry concern, as such events will eventually lead to alarms being ignored, rendering the leak detection system ineffective [1]. This paper discusses the recent work focused on the development of an innovative remote sensing technology that is capable of reliably and automatically detecting small hazardous liquid leaks in near real-time. The technology is suitable for airborne applications, including manned and unmanned aircraft, ground applications, as well as stationary applications, such as monitoring of pipeline pump stations. While the focus of the development was primarily for detecting liquid hydrocarbon leaks, the technology also shows promise for detecting gas leaks. The technology fuses inputs from various types of optical sensors and applies machine learning techniques to reliably detect “fingerprints” of small hazardous liquid leaks. The optical sensors used include long-wave infrared, short-wave infrared, hyperspectral, and visual cameras. The utilization of these different imaging approaches raises the possibility for detecting spilled product from a past event even if the leak is not actively progressing. In order to thoroughly characterize leaks, tests were performed by imaging a variety of different types of hazardous liquid constitutions (e.g. crude oil, refined products, crude oil mixed with a variety of common refined products, etc.) in several different environmental conditions (e.g., lighting, temperature, etc.) and on various surfaces (e.g., grass, pavement, gravel, etc.). Tests were also conducted to characterize non-leak events. Focus was given to highly reflective and highly absorbent materials/conditions that are typically found near pipelines. Techniques were developed to extract a variety of features across the several spectral bands to identify unique attributes of different types of hazardous liquid constitutions and environmental conditions as well as non-leak events. The characterization of non-leak events is crucial in significantly reducing false alarm rates. Classifiers were then trained to detect small leaks and reject non-leak events (false alarms), followed by system performance testing. The trial results of this work are discussed in this paper.

Application of the Dither Technology in a Fault Detection System

2013 ◽  
Vol 281 ◽  
pp. 71-74
Author(s):  
Na Chen ◽  
Shao Pu Yang ◽  
Cun Zhi Pan
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Detection System ◽  
Signal To Noise Ratio ◽  
Sensor Signal ◽  
Signal To Noise ◽  
Sampling System ◽  
System A ◽  
Fault Diagnosis System ◽  
Direct Guidance

In a fault detection system A/D conversion is a necessary step, in which quantization issues are unavoidable. Problems about quantization effects can be solved properly by using the dither technique. Firstly quantization problems of A/D conversion in a fault diagnosis system are discussed. Then the principle of dithering technique is introduced from the view of probability statistics. In further more, it is tested that fault signals whose amplitude is less than the quantization interval can be extracted, and that coherent harmonic interference in quantizing process can also be eliminated. Finally the result shows that by using dither technique the system can gain an enhanced level of fault detection with a faint signal-to-noise ratio loss, which has a direct guidance on engineering design in sensor-signal-sampling system.

scholarly journals Ensemble-based machine learning approach for improved leak detection in water mains

2021 ◽  
Author(s):  
Thambirajah Ravichandran ◽  
Keyhan Gavahi ◽  
Kumaraswamy Ponnambalam ◽  
Valentin Burtea ◽  
S. Jamshid Mousavi
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Leak Detection ◽  
Acoustic Signals ◽  
False Positives ◽  
Classification Model ◽  
Series Data ◽  
Gradient Boosting ◽  
Validation Data ◽  
Water Mains

Abstract This paper presents an acoustic leak detection system for distribution water mains using machine learning methods. The problem is formulated as a binary classifier to identify leak and no-leak cases using acoustic signals. A supervised learning methodology has been employed using several detection features extracted from acoustic signals, such as power spectral density and time-series data. The training and validation data sets have been collected over several months from multiple cities across North America. The proposed solution includes a multi-strategy ensemble learning (MEL) using a gradient boosting tree (GBT) classification model, which has performed better in maximizing detection rate and minimizing false positives as compared with other classification models such as KNN, ANN, and rule-based techniques. Further improvements have been achieved using a multitude of GBT classifiers combined in a parallel ensemble method called bagging algorithm. The proposed MEL approach demonstrates a significant improvement in performance, resulting in a reduction of false positives reports by an order of magnitude.

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