Experimental results of a waste-heat powered thermoacoustic refrigeration system for ships

Greenhouse gas emissions are a major concern for maritime transport. The International Maritime Organization (IMO) presents Waste Heat Recovery Systems (WHRS) from engine exhaust gas as a viable solution to improve energy utilization and reduce greenhouse gas emissions for marine power plants. In this paper, we present an original thermoacoustic WHRS and its associated experimental setup for validation. This WHRS aims to transport heat from a lower to a higher temperature reservoir (heat pump) and maintain a fridge at 0 °C. For the experimental setup, exhaust gases from a 4-stroke marine diesel engine are used to generate pressure waves (work) in thermoacoustic engines. The main result is the Coefficient of Performance (COP) of the system, around 0.3, assessed for various cold loads. The first results estimate that the system could substitute part of the chiller cold production onboard. Also, a preliminary economic analysis suggests that this system could perform 2% fuel savings that allow for a payback time below one year

Secured Architecture for Unmanned Surface Vehicle Fleets Management and Control

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset

Responding to the challenges of Water and Global Warming: Environmental Hydrogeology and Global Change Research Group (HYGLO-Lab)

[EN] The current Global Warming of planet Earth is probably the most important geological phenomenon in the last 20,000 years of its history and for human race. This process is having nowadays notable effects on the climate, ecosystems and natural resources. Possibly the most important renewable geological resource is water. One of the most strategic phases of the water cycle is groundwater. Despite its low visibility, quantitatively (and qualitatively too) it is essential for life on Planet Earth. Foreseeable consequences on groundwater due to climate change and sea level rise will be very significant. Hydrogeology can provide answers to many of the questions that are beginning to be raised in relation to these impacts and their effects. Environmental hydrogeology is a way of understanding the set of disciplines mixed in Hydrogeology as a Science of Nature. The HYGLO-Lab Research Group of the IGME-CSIC National Center attempts, through its lines of research, with a double global and local component, to provide answers to some of these questions.Peer reviewe

Détection de dysfonctionements et d'actes malveillants basée sur des modèles de qualité de données multi-capteurs

Naval systems represent a strategic infrastructure for international commerce and military activity. Their protection is thus an issue of major importance. Naval systems are increasingly computerized in order to perform an optimal and secure navigation. To attain this objective, on board vessel sensor systems provide navigation information to be monitored and controlled from distant computers. Because of their importance and computerization, naval systems have become a target for hackers. Maritime vessels also work in a harsh and uncertain operational environments that produce failures. Navigation decision-making based on wrongly understood anomalies can be potentially catastrophic.Due to the particular characteristics of naval systems, the existing detection methodologies can't be applied. We propose quality evaluation and analysis as an alternative. The novelty of quality applications on cyber-physical systems shows the need for a general methodology, which is conceived and examined in this dissertation, to evaluate the quality of generated data streams. Identified quality elements allow introducing an original approach to detect malicious acts and failures. It consists of two processing stages: first an evaluation of quality; followed by the determination of agreement limits, compliant with normal states to identify and categorize anomalies. The study cases of 13 scenarios for a simulator training platform of fuel tanks and 11 scenarios for two aerial drones illustrate the interest and relevance of the obtained results.Les systèmes navals représentent une infrastructure stratégique pour le commerce international et les activités militaires. Ces systèmes sont de plus en plus informatisés afin de réaliser une navigation optimale et sécurisée. Pour atteindre cet objectif, une grande variété de systèmes embarqués génèrent différentes informations sur la navigation et l'état des composants, ce qui permet le contrôle et le monitoring à distance. Du fait de leur importance et de leur informatisation, les systèmes navals sont devenus une cible privilégiée des pirates informatiques. Par ailleurs, la mer est un environnement rude et incertain qui peut produire des dysfonctionnements. En conséquence, la prise de décisions basée sur des fausses informations à cause des anomalies, peut être à l'origine de répercussions potentiellement catastrophiques.Du fait des caractéristiques particulières de ces systèmes, les méthodologies classiques de détection d'anomalies ne peuvent pas être appliquées tel que conçues originalement. Dans cette thèse nous proposons les mesures de qualité comme une potentielle alternative. Une méthodologie adaptée aux systèmes cyber-physiques a été définie pour évaluer la qualité des flux de données générés par les composants de ces systèmes. À partir de ces mesures, une nouvelle approche pour l'analyse de scénarios fonctionnels a été développée. Des niveaux d'acceptation bornent les états de normalité et détectent des mesures aberrantes. Les anomalies examinées par composant permettent de catégoriser les détections et de les associer aux catégories définies par le modèle proposé. L'application des travaux à 13 scénarios créés pour une plate-forme composée par deux cuves et à 11 scénarios pour deux drones aériens a servi à démontrer la pertinence et l'intérêt de ces travaux

Detection of dysfunctions and malveillant acts based on multi-sensor data quality models

Les systèmes navals représentent une infrastructure stratégique pour le commerce international et les activités militaires. Ces systèmes sont de plus en plus informatisés afin de réaliser une navigation optimale et sécurisée. Pour atteindre cet objectif, une grande variété de systèmes embarqués génèrent différentes informations sur la navigation et l'état des composants, ce qui permet le contrôle et le monitoring à distance. Du fait de leur importance et de leur informatisation, les systèmes navals sont devenus une cible privilégiée des pirates informatiques. Par ailleurs, la mer est un environnement rude et incertain qui peut produire des dysfonctionnements. En conséquence, la prise de décisions basée sur des fausses informations à cause des anomalies, peut être à l'origine de répercussions potentiellement catastrophiques.Du fait des caractéristiques particulières de ces systèmes, les méthodologies classiques de détection d'anomalies ne peuvent pas être appliquées tel que conçues originalement. Dans cette thèse nous proposons les mesures de qualité comme une potentielle alternative. Une méthodologie adaptée aux systèmes cyber-physiques a été définie pour évaluer la qualité des flux de données générés par les composants de ces systèmes. À partir de ces mesures, une nouvelle approche pour l'analyse de scénarios fonctionnels a été développée. Des niveaux d'acceptation bornent les états de normalité et détectent des mesures aberrantes. Les anomalies examinées par composant permettent de catégoriser les détections et de les associer aux catégories définies par le modèle proposé. L'application des travaux à 13 scénarios créés pour une plate-forme composée par deux cuves et à 11 scénarios pour deux drones aériens a servi à démontrer la pertinence et l'intérêt de ces travaux.Naval systems represent a strategic infrastructure for international commerce and military activity. Their protection is thus an issue of major importance. Naval systems are increasingly computerized in order to perform an optimal and secure navigation. To attain this objective, on board vessel sensor systems provide navigation information to be monitored and controlled from distant computers. Because of their importance and computerization, naval systems have become a target for hackers. Maritime vessels also work in a harsh and uncertain operational environments that produce failures. Navigation decision-making based on wrongly understood anomalies can be potentially catastrophic.Due to the particular characteristics of naval systems, the existing detection methodologies can't be applied. We propose quality evaluation and analysis as an alternative. The novelty of quality applications on cyber-physical systems shows the need for a general methodology, which is conceived and examined in this dissertation, to evaluate the quality of generated data streams. Identified quality elements allow introducing an original approach to detect malicious acts and failures. It consists of two processing stages: first an evaluation of quality; followed by the determination of agreement limits, compliant with normal states to identify and categorize anomalies. The study cases of 13 scenarios for a simulator training platform of fuel tanks and 11 scenarios for two aerial drones illustrate the interest and relevance of the obtained results

Impact Assessment of Anomaly Propagation in a Naval Water Distribution Cyber-Physical System

International audienceCyber-Physical Systems (CPS) are composed by multiple subsystems that encompass numerous interdependencies. Although indispensable and highly performant from a functional perspective, complex interconnectivity constitutes paradoxically a significant vulnerability when an anomaly occurs. Anomalies could propagate and impact the entire CPS with irreversible consequences. This paper presents an approach to assess the anomaly propagation impact risk on a three layers oriented graph which represents the physical, digital, and system variables of a CPS components and interdependencies. Anomalies are detected applying information quality measures, while potential propagation paths are assessed computing the cumulated risk represented by weights assigned to the graph edges. To verify the cascading impact of different anomalies four cyber-attacks - denial of service, sensor offset alteration, false data injection, and replay attack - were implemented on a simulated naval water distribution CPS. The propagation impact of three anomalies was successfully assessed and the corresponding estimated propagation path, if applicable, confirmed

Experimental results of a waste-heat powered thermoacoustic refrigeration system for ships

Greenhouse gas emissions are a major concern for maritime transport. The International Maritime Organization (IMO) presents Waste Heat Recovery Systems (WHRS) from engine exhaust gas as a viable solution to improve energy utilization and reduce greenhouse gas emissions for marine power plants. In this paper, we present an original thermoacoustic WHRS and its associated experimental setup for validation. This WHRS aims to transport heat from a lower to a higher temperature reservoir (heat pump) and maintain a fridge at 0 °C. For the experimental setup, exhaust gases from a 4-stroke marine diesel engine are used to generate pressure waves (work) in thermoacoustic engines. The main result is the Coefficient of Performance (COP) of the system, around 0.3, assessed for various cold loads. The first results estimate that the system could substitute part of the chiller cold production onboard. Also, a preliminary economic analysis suggests that this system could perform 2% fuel savings that allow for a payback time below one year

Analysis of Quality Measurements to Categorize Anomalies in Sensor Systems

International audienceSensor networks are becoming ubiquitous, enabling to improve decision-making and reducing human interaction by means of automatic or semi-automatic responses. However, due to deterioration or induced effects, sensors measures can be affected and produce anomalies that could alter decision-making. Most of the existing methods to identify sensors irregularities focus basically on detecting and discarding anomalous values, without looking for complementary information to understand generated anomalies. This paper presents an approach to obtain such complementary information by categorizing sensor anomalies, based on multidimensional quality assessment. It consists of two processing stages: an evaluation of data and information streams to estimate data quality imperfections and information quality dimensions; followed by the determination of agreement limits, compliant with normal states, to identify and categorize anomalies. The case study of discrete and analog sensors system installed in a simulator training platform of fuel tanks is presented, to illustrate an application of the proposed approach, considering 13 experimentally evaluated anomalies