Cloud-Based Water Leakage Detection and Localization

Shravani, Deeksha; Prajwal, Y R; Prapulla, S B; S, Girish Rao Salanke N; Shobha, G

doi:10.1109/ccem48484.2019.00018

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Further, Shravani et al developed a hybrid model mechanism to detect a leak in the data collected from flow rate sensors by combining MLP with SVM (Shravani et al 2019a).…”

Section: Introductionmentioning

confidence: 99%

Water Pipeline Leak Detection and Localization With 1DCNN and Novel Adaboost Classifier

Kothandaraman

Rajasekaran

Pua

2023

Preprint

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A pipeline leak localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. Recent advancements include machine learning and deep learning techniques to detect a leak with great accuracy, and cross-correlation is the widely used tool for localization. All the existing algorithms use two architectures to detect and localize a leak. The main aim of this paper is to propose a standalone architecture for leak detection and localization with 1DCNN and novel AdaBoost architecture. The data collected from a real-time pipeline setup is redundant, very dynamic, and contains a substantial amount of surrounding noise. So an appropriate feature extraction technique helps to capture essential information from pipeline data. In the proposed method, the 1DCNN extracts the essential features from the data collected from the real-time pipeline setup located in UTAR, Malaysia, using an Acousto-optic vibration sensor. The proposed novel AdaBoost architecture localizes the leak with the help of the features extracted using 1DCNN. The performance metrics used in this paper to evaluate the proposed method are training accuracy, validation accuracy, precision, recall, F1 score, and R-squared, and the results are 99.23, 97.26, 98.18, 98.19, 98.14, and 98.55, respectively which is better compared to the implemented existing method. The results show that the proposed 1DCNN-novel AdaBoost localizes the leak with high training and validation accuracy and performs well in other metrics.

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“…Further, Shravani et al developed a hybrid model mechanism to detect a leak in the data collected from flow rate sensors by combining MLP with SVM (Shravani et al 2019a).…”

Section: Introductionmentioning

confidence: 99%

Water Pipeline Leak Detection and Localization With 1DCNN and Novel Adaboost Classifier

Kothandaraman

Rajasekaran

Pua

2023

Preprint

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“…A few monitoring devices used in pipeline leak detection and localization are acoustic emission sensors [8][9][10][11][12][13][14][15][16][17][18][19], infrared thermography [20,21], microphones [22], CCTV, vibration sensors [23,24], accelerometers [25], pressure sensors [26][27][28][29], flow meters [30,31], and pipeline robots [32]. The acoustic emission and vibration sensors capture the vibration in the pipeline.…”

Section: Introductionmentioning

confidence: 99%

“…Widely used machine learning (ML) and DL algorithms to detect a pipeline leak are the support vector machine (SVM) [15,[29][30][31], naïve Bayes (NB) [31], logistic regression (LR), decision tree (DT) [24,30,31,33], multi-layer perceptron (MLP), k-nearest neighbors (KNN) [24,34], random forest (RF) [18,24], gradient boosting [24], LightGBM [24], XG-Boost [24], CatBoost [24], long short-term memory (LSTM) [35], and convolutional neural network (CNN) [8][9][10]14,16,17,[20][21][22][23][36][37][38][39][40][41][42]. Data collected from acoustic emission sensors, acousto-optic sensors, microphones, and vibration sensors must be feature-extracted for ML classifiers like SVM, NB, and DT.…”

Section: Introductionmentioning

confidence: 99%

“…Shravani et al compared LR, DT, NB, SVM, and MLP to identify the best classifier to detect a leak using flow meters and obtained an accuracy of 95% with LR [31]. Further, Shravani et al implemented a hybrid approach using SVM and MLP to improve flow meter-based leak detection accuracy [30]. The hybrid SVM-MLP combination gave a leak detection accuracy of 99%.…”

Section: Introductionmentioning

confidence: 99%

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Novel EMD with Optimal Mode Selector, MFCC, and 2DCNN for Leak Detection and Localization in Water Pipeline

Rajasekaran,

Kothandaraman,

Pua

2023

Applied Sciences

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Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods’ complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method’s leak detection-only credibility. This paper also implements EMD variants to show the novel EMD’s importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method’s prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%.

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