Techniques for locating service faults in mobile ad hoc networks

Fault localization in general refers to a technique for identifying the likely root causes of failures observed in systems formed from components. Fault localization in systems deployed on mobile ad hoc networks (MANETs) is a particularly challenging task because those systems are subject to a wider variety and higher incidence of faults than those deployed in xed networks, the resources available to track fault symptoms are severely limited, and many of the sources of faults in MANETs are by their nature transient. We present a method for localizing the faults occurring in service-based systems hosted on MANETs. The method is based on the use of dependence data that are discovered dynamically through decentralized observations of service interactions. We employ both Bayesian and timing-based reasoning techniques to analyze the data in the context of a speci c fault propagation model, deriving a ranked list of candidate fault locations. We present the results of an extensive set of experiments exploring a wide range of operational conditions to evaluate the accuracy of our method

Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Kalman filters for leak diagnosis in pipelines: brief history and future research

The purpose of this paper is to provide a structural review of the progress made on the detection and localization of leaks in pipelines by using approaches based on the Kalman filter. To the best of the author’s knowledge, this is the first review on the topic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, the paper gathers the results from different research groups such that these are presented in a unified fashion. For this reason, a classification of the current approaches based on the Kalman filter is proposed. For each of the existing approaches within this classification, the basic concepts, theoretical results, and relations with the other procedures are discussed in detail. The review starts with a short summary of essential ideas about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, brief discussions of some emerging research problems, such as the leak detection in pipelines transporting heavy oils; the main challenges; and some open issues are addressed

Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments

Prognostics and Health Management of PEMFC - state of the art and remaining challenges.

International audienceFuel Cell systems (FC) represent a promising alternative energy source. However, even if this technology is close to being dustrial deployment: FC still must be optimized, particularly by increasing their limited lifespan. This involves a better understanding of wearing processes and requires emulating the behavior of the whole system. Furthermore, a new area of science and technology emerges: Prognostics and Health Management (PHM) appears to be of great interest to face the problems of health assessment and life prediction of FCs. According to this, the aim of this paper is to present the current state of the art on PHM of FCs, more precisely of Proton-Exchange Membrane Fuel Cells (PEMFC) stack. PHM discipline is described in order to depict the processing layers that allow early deviations detection, avoiding faults, deciding mitigation actions, and thereby increasing the useful life of FCs. On this basis, a taxonomy of existing works on PHM of PEMFC is given, highlighting open problems to be addressed. The whole enables getting a better understanding of remaining challenging issues in this area

Fault localization in service-based systems hosted in mobile ad hoc networks

Fault localization in general refers to a technique for identifying the likely root causes of failures observed in systems formed from components. Fault localization in systems deployed on mobile ad hoc networks (MANETs) is a particularly challenging task because those systems are subject to a wider variety and higher incidence of faults than those deployed in fixed networks, the resources available to track fault symptoms are severely limited, and many of the sources of faults in MANETs are by their nature transient. We present a suite of three methods, each responsible for part of the overall task of localizing the faults occurring in service-based systems hosted on MANETs. First, we describe a dependence discovery method, designed specifically for this environment, yielding dynamic snapshots of dependence relationships discovered through decentralized observations of service interactions. Next, we present a method for localizing the faults occurring in service-based systems hosted on MANETs. We employ both Bayesian and timing-based reasoning techniques to analyze the dependence data produced by the dependence discovery method in the context of a specific fault propagation model, deriving a ranked list of candidate fault locations. In the third method, we present an epidemic protocol designed for transferring the dependence and symptom data between nodes of MANET networks with low connectivity. The protocol creates network wide synchronization overlay and transfers the data over intermediate nodes in periodic synchronization cycles. We introduce a new tool for simulation of service-based systems hosted on MANETs and use the tool for evaluation of several operational aspects of the methods. Next, we present implementation of the methods in Java EE and use emulation environment to evaluate the methods. We present the results of an extensive set of experiments exploring a wide range of operational conditions to evaluate the accuracy and performance of our methods.Open Acces

Real-Time Machine Learning Models To Detect Cyber And Physical Anomalies In Power Systems

A Smart Grid is a cyber-physical system (CPS) that tightly integrates computation and networking with physical processes to provide reliable two-way communication between electricity companies and customers. However, the grid availability and integrity are constantly threatened by both physical faults and cyber-attacks which may have a detrimental socio-economic impact. The frequency of the faults and attacks is increasing every year due to the extreme weather events and strong reliance on the open internet architecture that is vulnerable to cyber-attacks. In May 2021, for instance, Colonial Pipeline, one of the largest pipeline operators in the U.S., transports refined gasoline and jet fuel from Texas up the East Coast to New York was forced to shut down after being attacked by ransomware, causing prices to rise at gasoline pumps across the country. Enhancing situational awareness within the grid can alleviate these risks and avoid their adverse consequences. As part of this process, the phasor measurement units (PMU) are among the suitable assets since they collect time-synchronized measurements of grid status (30-120 samples/s), enabling the operators to react rapidly to potential anomalies. However, it is still challenging to process and analyze the open-ended source of PMU data as there are more than 2500 PMU distributed across the U.S. and Canada, where each of which generates more than 1.5 TB/month of streamed data. Further, the offline machine learning algorithms cannot be used in this scenario, as they require loading and scanning the entire dataset before processing. The ultimate objective of this dissertation is to develop early detection of cyber and physical anomalies in a real-time streaming environment setting by mining multi-variate large-scale synchrophasor data. To accomplish this objective, we start by investigating the cyber and physical anomalies, analyzing their impact, and critically reviewing the current detection approaches. Then, multiple machine learning models were designed to identify physical and cyber anomalies; the first one is an artificial neural network-based approach for detecting the False Data Injection (FDI) attack. This attack was specifically selected as it poses a serious risk to the integrity and availability of the grid; Secondly, we extend this approach by developing a Random Forest Regressor-based model which not only detects anomalies, but also identifies their location and duration; Lastly, we develop a real-time hoeffding tree-based model for detecting anomalies in steaming networks, and explicitly handling concept drifts. These models have been tested and the experimental results confirmed their superiority over the state-of-the-art models in terms of detection accuracy, false-positive rate, and processing time, making them potential candidates for strengthening the grid\u27s security