A crucial component within any structural health monitoring system is a pipeline leak detection mechanism, vital for preventing avoidable water loss. Contemporary literature employs machine learning and deep learning for detecting pipeline leaks and cross-correlation for leak localization. The major drawbacks in the existing methodologies are that machine learning and deep learning methods need two different architectures for leak detection and localization, and the cross-correlation needs two sensors with a denoising technique. The primary objective of this paper is to deploy a unified architecture capable of executing both the detection and localization of a leak without any denoising technique and with a single sensor. The proposed technique utilizes the data collected using an Acousto-optic sensor with two different pressures. This paper proposes a novel custom one-dimensional time-series DenseNet for leak detection and localization. The proposed method gives better accuracies compared with the existing one-dimensional DenseNet-121, three different one-dimensional convolutional neural networks (1DCNN), and cross-correlation for two different pressure datasets. The proposed method’s processing time is thirteen times less than the existing one-dimensional DenseNet-121, with the observed average leak detection and localization accuracy of 99.08%. The results state that the proposed novel custom one-dimensional time-series DenseNet accurately detects and localizes the leak with less time.

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%.

A pipeline leak detection and localization technique is crucial in a structural health monitoring system to prevent water wastage at an early stage. The main aim of this approach is to propose a standalone architecture for leak detection and localization using a single sensor. The sensor used in this approach is an Acousto-optic vibration sensor, which is highly sensitive to capture the vibrations caused by the pipeline leak. The proposed standalone architecture contains two steps: 1) Feature extraction and 2) leak detection and localization. This approach uses a one-dimensional convolutional neural network (1DCNN) for feature extraction. This paper tunes the AdaBoost to have support vector machines (SVM), Decision Trees (DT), and multi-layer perceptron (MLP) instead of the inbuilt weak estimators to give improved performance. The modified AdaBoost detects and localizes the leak by classifying the leak locations. The proposed 1DCNN-modified AdaBoost’s performance is cross-verified with nine models and cross-correlation. All the models are tested with 200000 and 300000 Pascal pressure to check the stability. The proposed 1DCNN-modified AdaBoost outperforms all the other methods implemented in this research. In the future, this research can be extended with different leak sizes and pipeline materials and real-time pipeline environments with longer distances.