Leakage Detection Framework using Domain-Informed Neural Networks and Support Vector Machines to Augment Self-Healing in Water Distribution Networks

The reduction of water leakage is essential for ensuring sustainable and resilient water supply systems. Despite recent investments in sensing technologies, pipe leakage remains a significant challenge for the water sector, particularly in developed nations like the UK, which suffer from aging water infrastructure. Conventional models and analytical methods for detecting pipe leakage often face reliability issues and are generally limited to detecting leaks during nighttime hours. Moreover, leakages are frequently detected by the customers rather than the water companies. To achieve substantial reductions in leakage and enhance public confidence in water supply and management, adopting an intelligent detection method is crucial. Such a method should effectively leverage existing sensor data for reliable leakage identification across the network. This not only helps in minimizing water loss and the associated energy costs of water treatment but also aids in steering the water sector towards a more sustainable and resilient future. As a step towards ‘self-healing’ water infrastructure systems, this study presents a novel framework for rapidly identifying potential leakages at the district meter area (DMA) level. The framework involves training a domain-informed variational autoencoder (VAE) for real-time dimensionality reduction of water flow time series data and developing a two-dimensional surrogate latent variable (LV) mapping which sufficiently and efficiently captures the distinct characteristics of leakage and regular (non-leakage) flow. The domain-informed training employs a novel loss function that ensures a distinct but regulated LV space for the two classes of flow groupings (i.e., leakage and non-leakage). Subsquently, a binary SVM classifier is used to provide a hyperplane for separating the two classes of LVs corresponding to the flow groupings. Hence, the proposed framework can be efficiently utilised to classify the incoming flow as leakage or non-leakage based on the encoded surrogates LVs of the flow time series using the trained VAE encoder. The framework is trained and tested on a dataset of over 2000 DMAs in North Yorkshire, UK, containing water flow time series recorded at 15-minute intervals over one year. The framework performs exceptionally well for both regular and leakage water flow groupings with a classification accuracy of over 98 % on the unobserved test datase

Acoustic Monitoring for Leaks in Water Distribution Networks

Water distribution networks (WDNs) are complex systems that are subjected to stresses due to a number of hydraulic and environmental loads. Small leaks can run continuously for extended periods, sometimes indefinitely, undetected due to their minimal impact on the global system characteristics. As a result, system leaks remain an unavoidable reality and water loss estimates range from 10\%-25\% between treatment and delivery. This is a significant economic loss due to non-revenue water and a waste of valuable natural resource. Leaks produce perceptible changes in the sound and vibration fields in their vicinity and this aspect as been exploited in various techniques to detect leaks today. For example, the vibrations caused on the pipe wall in metal pipes, or acoustic energy in the vicinity of the leak, have all been exploited to develop inspection tools. However, most techniques in use today suffer from the following: (i) they are primarily inspection techniques (not monitoring) and often involve an expert user to interpret inspection data; (ii) they employ intrusive procedures to gain access into the WDN and, (iii) their algorithms remain closed and publicly available blind benchmark tests have shown that the detection rates are quite low. The main objective of this thesis is to address each of the aforementioned three problems existing in current methods. First, a technology conducive to long-term monitoring will be developed, which can be deployed year-around in live WDN. Secondly, this technology will be developed around existing access locations in a WDN, specifically from fire hydrant locations. To make this technology conducive to operate in cold climates such as Canada, the technology will be deployed from dry-barrel hydrants. Finally, the technology will be tested with a range of powerful machine learning algorithms, some new and some well-proven, and results published in the open scientific literature. In terms of the technology itself, unlike a majority of technologies that rely on accelerometer or pressure data, this technology relies on the measurement of the acoustic (sound) field within the water column. The problem of leak detection and localization is addressed through a technique called linear prediction (LP). Extensively used in speech processing, LP is shown in this work to be effective in capturing the composite spectrum effects of radiation, pipe system, and leak-induced excitation of the pipe system, with and without leaks, and thus has the potential to be an effective tool to detect leaks. The relatively simple mathematical formulation of LP lends itself well to online implementation in long-term monitoring applications and hence motivates an in-depth investigation. For comparison purposes, model-free methods including a powerful signal processing technique and a technique from machine learning are employed. In terms of leak detection, three data-driven anomaly detection approaches are employed and the LP method is explored for leak localization as well. Tests were conducted on several laboratory test beds, with increasing levels of complexity and in a live WDN in the city of Guelph, Ontario, Canada. Results form this study show that the LP method developed in this thesis provides a unified framework for both leak detection and localization when used in conjunction with semi-supervised anomaly detection algorithms. A novel two-part localization approach is developed which utilizes LP pre-processed data, in tandem with the traditional cross-correlation approach. Results of the field study show that the presented method is able to perform both leak-detection and localization using relatively short time signal lengths. This is advantageous in continuous monitoring situations as this minimizes the data transmission requirements, the latter being one of the main impediments to full-scale implementation and deployment of leak-detection technology

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Smart Flow Control Processes in Micro Scale

In recent years, microfluidic devices with a large surface-to-volume ratio have witnessed rapid development, allowing them to be successfully utilized in many engineering applications. A smart control process has been proposed for many years, while many new innovations and enabling technologies have been developed for smart flow control, especially concerning “smart flow control” at the microscale. This Special Issue aims to highlight the current research trends related to this topic, presenting a collection of 33 papers from leading scholars in this field. Among these include studies and demonstrations of flow characteristics in pumps or valves as well as dynamic performance in roiling mill systems or jet systems to the optimal design of special components in smart control systems

Adversarial training to improve robustness of adversarial deep neural classifiers in the NOvA experiment

The NOvA experiment is a long-baseline neutrino oscillation experiment. Consisting of two functionally identical detectors situated off-axis in Fermilab’s NuMI neutrino beam. The Near Detector observes the unoscillated beam at Fermilab, while the Far Detector observes the oscillated beam 810 km away. This allows for measurements of the oscillation probabilities for multiple oscillation channels, ν_µ → ν_µ, anti ν_µ → anti ν_µ, ν_µ → ν_e and anti ν_µ → anti ν_e, leading to measurements of the neutrino oscillation parameters, sinθ_23, ∆m^2_32 and δ_CP. These measurements are produced from an extensive analysis of the recorded data. Deep neural networks are deployed at multiple stages of this analysis. The Event CVN network is deployed for the purposes of identifying and classifying the interaction types of selected neutrino events. The effects of systematic uncertainties present in the measurements on the network performance are investigated and are found to cause negligible variations. The robustness of these network trainings is therefore demonstrated which further justifies their current usage in the analysis beyond the standard validation. The effects on the network performance for larger systematic alterations to the training datasets beyond the systematic uncertainties, such as an exchange of the neutrino event generators, are investigated. The differences in network performance corresponding to the introduced variations are found to be minimal. Domain adaptation techniques are implemented in the AdCVN framework. These methods are deployed for the purpose of improving the Event CVN robustness for scenarios with systematic variations in the underlying data

Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers