AI-based leak detection and localization system in water distribution infrastructures

Abstract

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

Description

AREA OF INVENTION

[0001] The present invention relates to intelligent monitoring systems for liquid distribution networks and, in particular, an integrated hardware and software system with artificial intelligence for detecting and locating leaks in pressurized water distribution lines. The invention lies at the interface between IoT-enabled sensors, hydraulic modeling, acoustic signal analysis, and machine anomaly detection. BACKGROUND OF THE INVENTION

[0002] Water supply infrastructure is vital for modern urban and rural communities, delivering drinking water from treatment plants to residential, commercial, and industrial consumers. However, leaks in such systems cause significant water losses, contributing to high operating costs, energy waste, and resource scarcity. Conventional leak detection methods—such as manual inspection, DMA (District Metered Area) flow analysis, and simple acoustic listening devices—are limited in their spatial coverage, real-time response, and accuracy in locating small or emerging leaks.

[0003] Recent advances in sensor technology enable the distributed acquisition of pressure, flow, and acoustic signatures along pipelines. However, these raw signals are often noisy and affected by demand fluctuations, pump operation, and environmental factors. This makes it difficult to distinguish leak signatures from normal operational variations. Furthermore, conventional signal processing-based leak detection often relies on static thresholds or predefined models that may not be generalizable to different pipeline topologies and operating conditions.

[0004] Artificial intelligence (AI), particularly machine learning (ML) and deep learning, has proven especially effective at understanding complex, nonlinear relationships within high-dimensional data streams. Integrating AI into distributed sensor networks and hydraulic models improves leak detection accuracy, reduces false alarms, and allows for the highly reliable localization of leaks, even in large or complex water distribution networks.

[0005] The present invention overcomes the shortcomings of conventional leak detection systems through a device-based architecture that integrates distributed multimodal sensor hardware with an AI-driven cloud analytics engine and a real-time visualization and alerting interface. The system enables adaptive, continuous learning from historical and live operational data, thus facilitating the rapid detection and precise localization of leaks. This improves the operational efficiency and sustainability of water infrastructure management.

[0006] Water distribution infrastructure forms the backbone of urban and rural water supply systems. It delivers treated water from central facilities to end users via an extensive network of interconnected pipes, valves, and storage reservoirs. These systems operate under pressure to ensure sufficient flow and supply across varying elevations and demand profiles. However, leaks in such systems remain a persistent and costly problem worldwide. Losses due to leaks not only result in wasted treated water but also contribute to increased energy consumption during pumping operations, accelerated infrastructure deterioration, and higher operating costs. According to industry reports, water utilities can lose between 15 and 30% of their total water production due to undetected leaks and pipe ruptures.This water loss, often referred to as "non-revenue water" (NRW), represents a significant inefficiency, particularly in water-scarce regions where water conservation is critical. Compounding the problem, many water distribution networks extend over hundreds or even thousands of kilometers, are often underground, and traverse varied terrain, making physical inspection difficult and labor-intensive.

[0007] Historically, leak detection relied largely on manual inspections and routine checks of pipelines. One of the earliest approaches to leak detection was based on simple visual observation, with maintenance teams looking for signs such as water accumulation on the ground surface, local vegetation growth, or erosion patterns. While these methods are inexpensive, they are highly reactive. Leaks are often only detected once they have reached a scale that causes visible surface manifestations. This reactive nature often means that by the time the leak is located, significant amounts of water have already been lost, and potentially substantial damage has occurred to surrounding infrastructure.

[0008] To improve manual inspection, acoustic leak detection methods have been introduced. These methods employ listening devices to capture the sound signatures of water escaping from a pipe under pressure. These sounds typically manifest as high-frequency vibrations that travel along the pipe walls and through the water column. Operators equipped with geophones, ground microphones, or leak sound correlators listen for characteristic acoustic patterns that indicate leaks. While acoustic methods are effective for metal pipes and in relatively quiet operating environments, they reach their limits with plastic pipes, which dampen leak sounds more effectively. They are also less reliable in environments with high levels of ambient noise, such as near roads, pumping stations, or industrial facilities.Furthermore, these systems often require trained personnel to interpret the acoustic data, and their accuracy can vary depending on soil conditions, pipe material, and water pressure.

[0009] Another widely used technique is District Metered Area (DMA) monitoring. This involves dividing a water distribution network into zones, each equipped with flow meters to measure water inflow and outflow. By comparing the recorded flows with expected consumption, water utilities can identify areas of water loss. While DMA-based analyses can narrow down leaks to a specific zone, the precise location of a leak cannot be determined without further investigation. Furthermore, DMA implementation requires significant investment in flow measurement infrastructure and can be challenging in networks with complex topologies or variable consumption patterns. Additionally, DMA analyses are typically performed using aggregated daily or hourly data, limiting their ability to detect small, intermittent leaks in real time.

[0010] Pressure transient analysis is also used for leak detection. It is based on the principle that leaks cause characteristic changes in the hydraulic pressure profile of a pipeline. By continuously monitoring pressure at multiple points in a network, utility companies can detect anomalies that indicate leaks or pipe ruptures. However, pressure signals are frequently influenced by legitimate operational events such as valve closures, pump starts, or sudden changes in demand, leading to false alarms. This necessitates complex filtering or model-based interpretation, which is often not sufficiently adaptable to varying conditions in conventional systems.

[0011] Thanks to advances in telemetry and SCADA (Supervisory Control and Data Acquisition) systems, utility companies can monitor flow, pressure, and other operational parameters in near real-time. These systems integrate data from various sensors into central control rooms, where human operators analyze patterns and issue maintenance orders. While SCADA systems improve situational awareness, their effectiveness in leak detection depends heavily on the operator's analytical skills and the underlying detection techniques, which are often rule- or threshold-driven. Fixed thresholds, while easy to implement, cannot adapt to dynamic operational changes such as seasonal demand fluctuations, leading to an excessive number of false alarms or missed leaks.

[0012] Despite the availability of these diverse leak detection techniques, several persistent drawbacks hinder their effectiveness. Many existing systems rely heavily on human expertise for interpretation, making them prone to subjectivity and inconsistent performance. Conventional systems typically operate with static models or predefined thresholds that cannot adapt to the complex, nonlinear relationships between pipeline operating parameters and leak signatures. Furthermore, most conventional systems focus on a single sensor modality—acoustics, pressure, or flow—limiting their ability to detect leaks under varying conditions.For example, small leaks can produce minimal acoustic signatures but measurable pressure changes, while bursting leaks generate strong acoustic signals but overwhelm pressure monitoring systems due to rapid transients. Without the integration of multiple sensor modalities, detection accuracy remains suboptimal.

[0013] Furthermore, existing solutions often fail to locate leaks effectively and precisely. While some systems can identify general problem areas, pinpointing the exact leak location typically requires additional on-site investigations, delaying repairs and increasing costs. In many cases, leak detection requires physical access to the pipeline and invasive testing methods that can disrupt the water supply and necessitate excavation. In large distribution networks, the time between initial detection and precise localization can be significant, allowing leaks to persist and propagate.

[0014] Another challenge lies in scalability. Many current systems work well in small pilot projects, but encounter difficulties when scaling to city-wide networks. Communication infrastructure, data storage capacity, and computing power often become bottlenecks, especially when high-frequency sensor data needs to be processed in real time. Older detection systems were not designed for modern cloud-based architectures and may lack the necessary interoperability for integration with new sensors or analytics platforms.

[0015] The increasing availability of cost-effective IoT (Internet of Things) sensors and advances in wireless communication technologies such as NB-IoT and LoRaWAN are opening up new possibilities for large-scale, distributed leak monitoring. However, deploying these sensors without intelligent analytics can lead to an overwhelming volume of data, making manual evaluation impractical. This underscores the need for automated, adaptive systems that can learn from historical and live data to distinguish between genuine leaks and harmless operational variations.

[0016] Artificial intelligence, particularly machine learning and deep learning, has proven to be a promising solution to these challenges. AI-based systems can process vast amounts of multimodal sensor data and learn to recognize subtle patterns that precede or accompany leaks. By training on historical leak events, supervised models can classify incoming data with high accuracy, while unsupervised models can identify novel anomalies without explicit labeling. Despite this potential, few commercial systems have fully integrated AI into their leak detection workflows. Many AI-based prototypes remain confined to academic research, where controlled testing environments cannot capture the full variability and complexity of real-world water networks.Furthermore, the integration of AI into pipeline topology models and hydraulic simulations is still limited, which restricts the ability not only to detect leaks but also to pinpoint their exact location.

[0017] Finally, continuous improvement remains a significant gap in most existing systems. Once implemented, conventional detection techniques often remain static, failing to incorporate feedback from confirmed leak repairs or false alarm investigations. This prevents the system from refining its models over time, resulting in persistent inefficiencies. In contrast, a continuously learning, AI-based system could adapt to changing network conditions, sensor aging, and seasonal demand fluctuations, thereby maintaining high accuracy and reliability over a longer operational lifetime.

[0018] Existing leak detection solutions offer varying degrees of effectiveness but are limited by issues such as restricted sensor modalities, static analysis models, human interpretation, poor scalability, and inadequate localization capabilities. These limitations result in delayed leak detection, high false alarm rates, and increased operating costs. There is a clear need for an integrated, AI-driven system that combines distributed multimodal sensing, adaptive analytics, and topology-aware leak localization to overcome these drawbacks and provide utilities with a scalable, accurate, and continuously improving solution for managing water infrastructure. Summary of the invention

[0019] The present invention describes a hardware- and software-integrated system for AI-supported leak detection and localization in water supply infrastructures. The system comprises several distributed sensor units—each with a pressure transmitter, a flow meter, and an acoustic sensor—strategically installed along the water pipeline. Each sensor unit is housed in a weatherproof, corrosion-resistant enclosure and features an embedded microcontroller, an analog-to-digital converter stage, and a wireless communication module that transmits time-synchronized sensor readings to a central, cloud-based AI engine.

[0020] The AI ​​engine comprises both supervised and unsupervised learning models. The supervised component, implemented using deep neural networks and gradient-enhanced decision trees, is trained on labeled historical datasets with and without leaks to classify incoming sensor patterns. The unsupervised component uses clustering and autoencoder-based anomaly detection to identify deviations from learned operational values ​​without prior labeling. A pipeline topology-oriented localization module uses Bayesian inference and time-of-arrival-time-difference (TDOA) analysis of acoustic signals, combined with hydraulic pressure wave modeling, to calculate the probable coordinates of the leak.

[0021] A system controller manages data acquisition, timing alignment, and sensor condition diagnostics. The cloud-based processing framework provides APIs for integration with a web-based monitoring dashboard that displays real-time leak probability maps, pressure / flow / acoustic trend graphs, and automatically generates maintenance tickets. The system also features adaptive learning capabilities, incorporating feedback from confirmed leak repairs to refine model parameters and improve long-term accuracy.

[0022] The main objective of the present invention is to provide an intelligent, integrated system for the highly accurate and low-latency detection and localization of leaks in water supply systems. The invention overcomes the inherent limitations of conventional leak detection methods by combining distributed multimodal sensor hardware—including pressure, flow, and acoustic sensors—with advanced artificial intelligence capable of processing large amounts of real-time data. A further objective of the invention is to ensure precise and robust leak detection under varying operating conditions and to reduce false alarms due to demand fluctuations, pump operation, or ambient noise. The system not only detects leaks but also determines their precise location within the pipeline network. This is achieved through the use of topology-based modeling, hydraulic wave propagation analysis, and acoustic signal correlation.

[0023] Another objective is to provide continuous learning capabilities that allow AI techniques to improve their predictive accuracy over time by incorporating feedback from confirmed leak repairs, seasonal consumption fluctuations, and evolving operational parameters. The invention also aims to enable scalable deployment in small to city-wide water networks. This is achieved by utilizing energy-efficient IoT communication protocols to efficiently transmit sensor data to a central processing platform without incurring excessive bandwidth or energy consumption. A further objective is the integration of detection and localization functions into a remote monitoring interface, providing operators with real-time visualization of leak probabilities, georeferenced location maps, historical trend analyses, and the automatic creation of maintenance tickets.

[0024] Furthermore, the invention is intended to enable proactive maintenance strategies by facilitating the early detection of small or developing leaks before they lead to major failures. This reduces water losses, prevents infrastructure damage, and lowers operating costs. The system is also designed to be compatible with existing SCADA (Supervisory Control and Data Acquisition) and GIS (Globally Appropriate Data Acquisition) platforms and ensure seamless integration into existing utility operations. The invention aims to promote sustainable water resource management by providing a reliable, adaptable, and technologically advanced solution that improves the efficiency, resilience, and longevity of water distribution infrastructure. BRIEF DESCRIPTION OF THE FIGURE

[0025] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of a system for AI-supported leak detection and localization in water distribution infrastructures.

[0026] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0027] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0028] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0029] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0030] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0032] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0033] Fig.Figure 1 shows a block diagram of a system for AI-assisted leak detection and localization in water distribution infrastructure. The system 100 comprises: an avionics data acquisition module (102) operationally coupled to at least one aircraft flight control system, the avionics data acquisition module being configured to receive real-time flight parameters such as altitude, airspeed, attitude, engine performance metrics, navigation coordinates, and environmental sensor inputs; a multimodal perception unit (104) comprising: (i) an optical sensor subsystem (104a) configured to acquire forward- and downward-facing high-resolution images; (ii) a radar / lidar fusion subsystem (104b) configured to generate three-dimensional terrain and obstacle maps; and (iii) an audio and vibration sensor subsystem configured to detect anomalous acoustic or structural vibration signatures.a context-aware decision inference engine (106) implemented using a hybrid neural-symbolic architecture, comprising: (i) a deep neural network model trained for sensor fusion, anomaly detection, and hazard classification; and (ii) a rule-based inference module configured to evaluate mission constraints, airspace regulations, and safety protocols; a trajectory optimization subsystem (108) configured to generate alternative flight route solutions using real-time multi-objective optimization techniques that balance fuel efficiency, safety margins, time constraints, and environmental impact; a pilot interaction and warning module (110) comprising: (i) a multimodal interface for acoustic, visual, and haptic feedback;and (ii) a tiered intervention mechanism that issues escalating alerts or partially autonomously assumes control when a predefined danger threshold is exceeded; and a secure data logging and telemetry unit (112) configured to cryptographically sign and store flight decision data for post-flight analysis, with all subsystems interconnected via a low-latency deterministic communication bus with redundant failover channels.

[0034] In one embodiment, the optical sensor subsystem (104a) also includes a hyperspectral imaging module configured to detect atmospheric anomalies such as microburst formations or ash clouds by spectral band analysis beyond the visible range.

[0035] In one embodiment, the radar / lidar fusion subsystem (104b) performs dynamic voxel-based environment modeling with temporal change tracking and is configured to update the obstacle map under operating conditions with a latency of at most 50 milliseconds.

[0036] In one embodiment, the context-related decision inference machine (106) also includes a causal inference layer configured to identify likely root causes of anomalies by integrating time-synchronized multi-sensor data streams, historical flight records, and maintenance logs.

[0037] In one embodiment, the flight path optimization subsystem (108) uses a predictive control framework with a rolling horizon model that recalculates optimal flight routes every 5 seconds using updated environment and aircraft state vectors, and wherein the subsystem maintains compliance with restrictions regarding restricted airspace and climb / descent rate limits.

[0038] In one embodiment, the pilot interaction and warning module (110) implements a three-stage intervention protocol, wherein a first stage issues warning messages, a second stage amplifies the pilot's control inputs via force feedback mechanisms on the control stick, and a third stage overrides manual controls for collision avoidance maneuvers, while intervention events are logged with a timestamp accuracy of ±1 millisecond.

[0039] In one embodiment, the secure data logging and telemetry unit (112) comprises a distributed ledger anchored in the blockchain for the immutable recording of AI-generated recommendations and pilot responses, with each aircraft being assigned asymmetric cryptographic key pairs to ensure source authentication.

[0040] In one embodiment, the avionics data acquisition module (102) comprises an FPGA-based preprocessing unit configured to perform hardware-accelerated normalization, error correction, and time synchronization of the sensor data prior to input into the multimodal perception unit, thereby reducing the CPU load by at least 40%.

[0041] The AI-powered leak detection and localization system for water distribution infrastructures operates through coordinated interaction between distributed sensor hardware, wireless communication modules, and a cloud-based artificial intelligence engine. Each sensor node is designed to withstand the environmental and operating conditions of buried or exposed pipeline installations. The housing of each node meets at least IP67 protection standards, ensuring dustproof and waterproof operation, and is constructed from corrosion-resistant materials such as anodized aluminum or reinforced polymer composites. The node integrates three primary sensor elements: a high-resolution pressure sensor capable of detecting hydraulic pressure changes with a resolution of at least 0.01 bar, an ultrasonic or electromagnetic flow sensor with an accuracy of ±0.The system is calibrated to 5% and features a piezoelectric acoustic transducer mounted directly on the pipe surface via a coupling medium to ensure efficient vibration transmission. The acoustic transducer is connected to a signal conditioning circuit that includes a multi-stage Butterworth bandpass filter tuned to a frequency band characteristic of leakage-induced vibrations. This suppresses low-frequency hydraulic disturbances and high-frequency ambient noise before analog-to-digital conversion.

[0042] The embedded microcontroller in each sensor node contains integrated analog-to-digital converters with selectable sampling rates, enabling the acquisition of both low-frequency pressure and flow measurements as well as high-frequency acoustic signatures within the same device. The microcontroller's firmware performs preprocessing of the raw sensor data, including median filtering of pressure signals to eliminate transient anomalies caused by pump starts or valve closures, adaptive downsampling of flow data to optimize bandwidth utilization, and lossless wavelet-based compression of acoustic signals to maintain diagnostic accuracy while minimizing packet size. Preprocessed and timestamped data is then encrypted and transmitted to the cloud platform via a multiprotocol wireless communication module that can operate over NB-IoT, LTE-M, or LoRaWAN networks.The module autonomously selects the optimal transmission protocol by evaluating connection quality metrics, available bandwidth, and the node's battery status. A power management subsystem dynamically switches between high-performance, low-performance, and standby modes based on local triggers or instructions from the AI ​​engine. This ensures a longer operating life without compromising detection sensitivity.

[0043] Upon receipt on the cloud-based processing platform, the data acquisition module decodes, validates, and precisely maps the sensor data streams from multiple nodes using GPS or network time protocol synchronization. The mapped data is then fed into a feature extraction module, which calculates various statistical, temporal, and spectral features from the input streams. For acoustic data, short-time Fourier transform (STFT) spectrograms are generated to provide a time-frequency representation that is particularly effective at distinguishing leakage-induced tonal components from broadband background noise. Additional features such as wavelet coefficients, spectral centroid, and kurtosis are calculated to capture the unique characteristics of leaks across different pipe materials and diameters.For pressure and flow data, features include moving average differences, rate of change metrics, and correlation patterns between neighboring nodes, which can reveal subtle hydraulic imbalances caused by leakage.

[0044] The supervised learning module processes these features using a convolutional neural network (CNN) optimized for acoustic spectrogram classification, combined with an ensemble of gradient-boosting decision trees for pressure and flow anomaly detection. The CNN is trained on a diverse dataset that includes both real leak recordings and synthetic leak signals generated using finite element acoustic simulations under various pipe diameters, materials, and pressure conditions. This hybrid dataset approach ensures robust detection even for network configurations not represented in field data. The gradient-boosting models are trained on labeled operational data to learn the complex relationships between hydraulic variables under normal and leakage conditions.This reduces the dependence on fixed thresholds and improves sensitivity to small leaks.

[0045] In parallel to the supervised path, the unsupervised learning module continuously models the normal operating baseline of the pipeline network. This module uses a neural autoencoder network with a bottleneck layer reduced to less than 5% of the input feature size. This forces the network to learn a compact representation of normal patterns. Autoencoder reconstruction errors are monitored in real time, and anomaly thresholds are dynamically adjusted to account for seasonal and diurnal demand fluctuations. These thresholds are calculated over a sliding historical window of at least 90 days of data, allowing the system to adapt to long-term changes without operator intervention. Clustering techniques such as DBSCAN can be applied to group anomalies and isolate recurring leak signatures from transient irregularities.

[0046] Leak localization is handled by the topology-aware localization module, which manages a graphical representation of the piping network. The nodes correspond to the sensor positions, and the edges represent the pipe segments with length, diameter, material type, and hydraulic impedance parameters. This module integrates acoustic time difference of arrival (TDOA) measurements from multiple nodes with pressure drop gradients calculated across the network. A Bayesian inference framework is applied to combine these heterogeneous measurements and generate a maximum a posteriori (MAP) estimate of the leak location. This estimate is further refined by simulating pressure wave propagation using the Joukowsky equation, enabling precise distance calculations from the detection nodes to the leak source.The result is a set of geocoordinates that correspond to the most likely leak location and can be directly assigned to the utility's geographic information system (GIS).

[0047] The remote monitoring interface presents these results via a GIS-enabled dashboard, accessible through web browsers and mobile devices. Leak probability heatmaps are overlaid on the pipeline topology, with interactive drill-down capabilities that display real-time and historical trends for each sensor node. The dashboard can generate automated maintenance tickets linked to the coordinates of the suspected leak. Priority levels are based on leak confidence levels and estimated flow rates derived from hydraulic models. Operators can acknowledge or dismiss alerts via the interface. This feedback is logged by the AI ​​engine to enable continuous learning.

[0048] Continuous learning is a core feature of the system. Feedback from confirmed leaks and false alarms is regularly integrated into the supervised learning module through incremental retraining. Incremental learning allows model weights to be updated without a complete retraining. This preserves existing knowledge and improves detection performance for new conditions. This adaptive cycle ensures that the system adjusts to real-world operational changes, sensor aging, and seasonal demand patterns. Over time, this results in an increasingly precise and reliable leak detection and localization platform that scales from small utility networks to large, complex, city-wide water distribution infrastructures.

[0049] This integrated combination of multimodal sensors, adaptive AI analysis, and topology-based localization represents a significant advancement over conventional leak detection systems, enabling utilities to quickly identify and locate leaks, reduce water losses, lower operating costs, and extend the lifespan of critical infrastructure. The system's ability to operate under varying conditions, adapt to changing network dynamics, and deliver precise georeferenced leak locations makes it a powerful tool for sustainable water resource management.

[0050] The system of the present invention comprises a network of distributed sensor nodes along the water supply line. Each sensor node (10) comprises: • A pressure sensor (11) is configured to measure the local static and dynamic pressure in the pipeline with a resolution of 0.01 bar. • A flow sensor (12), preferably ultrasonic or electromagnetic, capable of measuring the volume flow with a resolution of ±0.5% of the measured value. • An acoustic sensor (13), preferably a piezoelectric vibration transducer, is mounted to detect sound waves caused by leaks that propagate along the pipe wall or water column. • An embedded microcontroller unit (MCU) (14) with integrated analog-to-digital conversion (ADC) channels for sampling the sensor outputs at rates of up to 10 kHz for acoustic signals and 1 Hz for pressure and flow. • A wireless communication module (15), for example LTE-M, NB-IoT or LoRaWAN, for transmitting data packets to a cloud server. • A local energy subsystem (16) which may include a rechargeable lithium battery and optionally a photovoltaic energy harvesting system.

[0051] The sensor node is housed in a sealed enclosure (17) that meets at least IP67 protection standards and features mounting brackets for secure attachment to piping structures. The internal firmware is configured for local preprocessing, including signal filtering (Butterworth bandpass for acoustic data, median filtering for pressure / flow), data compression, and timestamp synchronization via GPS or Network Time Protocol (NTP).

[0052] All sensor nodes communicate with the central AI engine (20), which is hosted on a cloud-based processing platform. The AI ​​engine includes: • A data acquisition layer (21) for receiving, decoding and timing incoming sensor streams. • A feature extraction module (22) for deriving statistical, frequency domain-related and wavelet-based features from raw sensor signals. • A supervised learning module (23) comprising a convolutional neural network (CNN) for classifying acoustic leak signatures and an ensemble of gradient boosting models for detecting pressure / flow anomalies. • An unsupervised learning module (24) that uses autoencoders, clustering techniques (e.g. DBSCAN) and principal component analysis (PCA) to detect previously unseen leak patterns. • A localization module (25) implementing the modeling of pipeline topology graphs and equations for hydraulic wave propagation, combined with a TDOA estimation from acoustic multi-node detections to determine the most probable leak location.

[0053] The system controller (30) coordinates the AI ​​processing, manages the model parameter databases, and triggers leak alerts when reliability exceeds a dynamic threshold. The system supports continuous learning (31) by recording confirmation data after repair to mark historical data sets and thus improve model performance.

[0054] A remote monitoring interface (40) is provided, which is accessible via a web browser or a mobile application and displays the following: • Real-time network maps with georeferenced sensor locations. • Heatmaps showing leak probability, overlaid with the pipeline topology. • Time series diagrams of pressure, flow rate and acoustic measurements. • Automatic generation of maintenance tickets linked to the GIS coordinates of suspected leaks.

[0055] By integrating multimodal sensors, AI-driven analytics, and topology-aware leak detection, a robust and scalable system is created that can detect both catastrophic pipe bursts and small, incipient leaks, reducing water loss, improving operational efficiency, and extending the lifespan of the distribution infrastructure.

[0056] The invention relates to avionics, in particular intelligent flight assistance systems for manned aircraft. It relates to a real-time copilot decision support platform that integrates multimodal sensor fusion, machine learning situation prediction, and adaptive recommendation mechanisms for operational safety in flight. The system utilizes advanced AI techniques, temporal data synchronization techniques, and pilot-interactive interfaces to improve decision-making in normal and emergency flight operations. It is particularly applicable in commercial aviation, military aircraft, and general aviation, which require highly reliable operations in complex airspace environments.

[0057] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0058] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A system for AI-supported leak detection and localization in water distribution infrastructures. 102 Avionics Data Acquisition Module 104 Multimodal Perception Unit 104a Optical Sensor Subsystem 104b radar / lidar fusion subsystem 106 Context-based decision inference engine 108 Subsystem for trajectory optimization 110 Pilot Interaction and Alerting Module 112 Secure Data Acquisition and Telemetry Unit

Claims

[1] A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface. [2] System according to claim 1, wherein the pressure sensor, the flow sensor and the acoustic sensor are mounted in a sealed housing that meets at least the IP67 protection standard, and wherein the acoustic sensor comprises a piezoelectric transducer that is mechanically coupled to the outer surface of the pipeline, wherein the transducer is connected to the microcontroller via a signal conditioning circuit which includes a multi-stage Butterworth bandpass filter tuned to a leak signature frequency band, such that external ambient vibrations and low-frequency hydraulic transients are attenuated prior to digitization. [3] System according to claim 1, wherein the supervised learning module comprises a Convolutional Neural Network (CNN) configured to process short-time Fourier transform (STFT) spectrograms of acoustic data for classification, and wherein the CNN is trained on a dataset enhanced by synthetic leak signatures generated by acoustic finite element simulation of various pipe materials and diameters, thereby improving detection accuracy in pipeline configurations not represented in historical field data. [4] System according to claim 1, wherein the unsupervised learning module comprises a neural autoencoder network with a bottleneck layer dimension reduced to less than 5% of the original input feature vector size, so that reconstruction error thresholds are dynamically adjusted based on seasonal demand fluctuation patterns derived from a sliding historical window of at least 90 days of pipeline operating data, thereby enabling adaptive anomaly detection under variable operating conditions. [5] System according to claim 1, wherein the embedded microcontroller executes firmware instructions to perform local preprocessing of the sensor data prior to wireless transmission. The preprocessing includes median filtering of the pressure readings to suppress transient spikes caused by pump starts, adaptive downsampling of the flow data to save transmission bandwidth, and compression of acoustic data streams using lossless wavelet-based encoding to maintain the integrity of high-frequency leak signatures while reducing packet size. [6] System according to claim 1, wherein the wireless communication is configured to automatically switch between protocols based on link quality indicators, available bandwidth and power consumption limitations, so that the system maintains reliable data transmission over heterogeneous network coverage areas without exceeding a predefined daily energy budget for each sensor node. [7] System according to claim 1, wherein the remote monitoring interface comprises a dashboard implemented as a web application with a geographic information system (GIS), wherein the dashboard is configured to display real-time heatmaps with leak probabilities over the pipeline layout, stream interactive time series graphs of pressure, flow and acoustic sensor values ​​and generate automated maintenance tickets tagged with geocoordinates of suspected leaks, wherein the ticket priority levels are calculated based on leak confidence ratings and estimated leak discharge rates derived from hydraulic models. [8] System according to claim 1, wherein the artificial intelligence engine is configured for continuous learning by incorporating operator feedback from confirmed leak repairs or false alarm reports into the supervised learning module, wherein the retraining process is carried out systematically using incremental learning techniques to update model weights without requiring a complete retraining from scratch, thereby preserving prior knowledge while improving detection accuracy for newly observed leak conditions. [9] System according to claim 1, wherein the power subsystem further comprises a power management circuit configured to operate in three modes: a high-performance mode that enables full-frequency acoustic sampling in the event of suspected leaks; a low-performance mode that enables periodic pressure and flow monitoring at reduced sampling rates; and a standby mode that is activated when the network is inactive, with the system autonomously switching between modes based on event triggers from the AI ​​engine or local anomaly detection at the sensor node, thus extending the operating lifetime without compromising detection capability.

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