Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants

This work proposes a data fusion approach for quickest fault detection and localization within industrial plants via wireless sensor networks. Two approaches are proposed, each exploiting different network architectures. In the first approach, multiple sensors monitor a plant section and individually report their local decisions to a fusion center. The fusion center provides a global decision after spatial aggregation of the local decisions. A post-processing center subsequently processes these global decisions in time, which performs quick detection and localization. Alternatively, the fusion center directly performs a spatio-temporal aggregation directed at quickest detection, together with a possible estimation of the faulty item. Both architectures are provided with a feedback system where the network’s highest hierarchical level transmits parameters to the lower levels. The two proposed approaches model the faults according to a Bayesian criterion and exploit the knowledge of the reliability model of the plant under monitoring. Moreover, adaptations of the well-known Shewhart and CUSUM charts are provided to fit the different architectures and are used for comparison purposes. Finally, the algorithms are tested via simulation on an active Oil and Gas subsea production system, and performances are provided.acceptedVersio

Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario

In this work, we present a spatio-temporal decision fusion approach aimed at performing quickest detection of faults within an Oil and Gas subsea production system. Specifically, a sensor network collectively monitors the state of different pieces of equipment and reports the collected decisions to a fusion center. Therein, a spatial aggregation is performed and a global decision is taken. Such decisions are then aggregated in time by a post-processing center, which performs quickest detection of system fault according to a Bayesian criterion which exploits change-time statistical distributions originated by system components’ datasheets. The performance of our approach is analyzed in terms of both detection- and reliability-focused metrics, with a focus on (fast & inspection-cost-limited) leak detection in a real-world oil platform located in the Barents Sea.acceptedVersio

Subsea Risk Management Based on Sensor Networks

Denne masteroppgaven omhandler evalueringen av undervannsrisikostyring for å forhindre oljeutslipp basert på analyse av informasjon mottatt fra sensorer i et underwater distributed sensor network. Oppgaven begynner med en fremheving av viktigheten av et velfungerende lekkasjedeteksjonssystem både fra et miljø- og et sikkerhetsperspektiv. Goliat FPSO betraktes som en casestudie. Gjeldende FPSO er plassert i Barentshavet, hvilket er et sensitivt område og må oppfylle krav diktert av norske myndigheter for å forhindre oljeutslipp. En innovativ teknologi benyttes på denne plattformen for å detektere mulig oljeutslipp under vann: bruken av passive akustiske sensorer. Et sensornettverk slik som dette består av ulike sensorer som sender informasjon (lokal beslutning) til et fusjonssenter som tar en global beslutning vedrørende om lekkasjen pågår eller ikke. Denne oppgaven vil evaluere hvordan valget av ulike fusjonsregler (Counting Rule og Weighted Fusion Rule tilpasset denne oppgaven) kan påvirke yteevnen til lekkasjedeteksjonssystemet i dets gjeldende konfigurasjon. Det vil også bli diskutert hvordan forskjellige terskler, valgt for en spesifikk fusjonsregel eller sensortest, kan endre den endelige yteevnen sett fra et deteksjonsperspektiv. Deteksjonsmetoder baseres på statistisk signalprosessering og beslutningsteori, ofte ved utnyttelse av allerede eksisterende metoder brukt i andre felt (telekommunikasjonsteknologi, medisin, krigsvitenskap), som må tilpasses til dette bruksområdet innen olje -og gassindustrien. Et steg videre er å utvikle en metode som kan lokalisere lekkasjepunktet i en havbunnsramme. Denne oppgaven foreslår noen metoder som kan være nyttige for å lokalisere utstyret ansvarlig for lekkasjen. Disse foreslåtte metodene for lekkasjelokalisering er utviklet så de kan jobbe sammen med de foreslåtte metodene for lekkasjedeteksjon, hvilket vil gi et koherent sett av operasjoner som sensorene og fusjonssenteret må utføre. Yteevnen til deteksjonsteknikkene bestemmes ut ifra en balanse mellom behovet for høyere verdier av parametere som Sanne Positive Rate og Presisjon, og å beholde lave verdier av Falske Positive Rate. Yteevnen til lokaliseringsteknikkene vil bli evaluert ut ifra deres evne til å lokalisere lekkasjepunkter i løpet av kortest mulig tid. Hvis dette ikke er mulig vil andre parametere tas i betraktning, som for eksempel differansen mellom estimert posisjon og faktisk lekkasjeposisjon. Noen flere simuleringer utføres for å teste de foreslåtte lokale og globale tersklene brukt sammen med spesifikke fusjonsregler for deteksjon og lokalisering av lekkasjen. Yteevnen til de ulike konfigurasjonene vil bli rangert i henhold til globale indekser nødvendige for å samle ovennevnte deteksjonsevneparametere. Disse indeksene kan baseres enten på ROC-kurven (som Youdens indeks) eller på PR-kurven (som F-mål)

Subsea Oil Spill Risk Management based on Sensor Networks

This thesis consists of the evaluation of sensor-based risk management against oil spills using an underwater distributed sensor network. The work starts by highlighting the importance of having a performing leak detection system both from an environmental, safety and economic point of view. The case study is the Goliat FPSO in the Barents Sea which has to meet requirements dictated by Norwegian authorities to prevent oil spills. The modeled network is made of passive acoustic sensors monitoring the subsea manifolds. These sensors send their local 1-bit decision to a Fusion Center which takes a global decision on whether the leakage is occurring. This work evaluates how the choice of adapted Fusion Rules (Counting Rule and Weighted Fusion Rule) can affect the performances of the leak detection system in its current geometry. It will also be discussed how different thresholds, selected for a specific FR or sensor test, can change the system performance. The detection methods are based on statistical signal processing adapted to fit this application within the Oil&Gas field. The work also proposes some new leak localization methods developed so they can be coupled with the proposed leak detection methods, giving a coherent set of operations that the sensors and the FC must perform. Performances of detection techniques are assessed balancing the need for high values of True Positive Rate and Precision and low values of False Positive Rate using indexes based both on the ROC curve (like the Youden's Index) and on the PR curve (the F-scores). Whereas, performances of localization techniques will be assessed on their ability to localize the spill in the shortest time; if this is not possible, parameters like the difference between the estimated and the real leak position will be considered. Finally, some tests are carried out applying the different sets of proposed methods

Data Fusion for Subsea Oil Spill Detection Through Wireless Sensor Networks

This work studies the impact of Wireless Sensor Networks (WSNs) for oil spill detection in subsea Oil&Gas applications. The case study is the Goliat FPSO where one WSN with passive acoustic sensors is assumed to be installed on each subsea template to monitor the manifold. Sensors take local binary decisions regarding the presence/absence of a spill by performing an energy test. A Fusion Center (FC) collects such local decisions and provides a more reliable global binary decision. The Counting Rule (CR) and a modified Chair-Varshney Rule (MCVR) are compared. An objective function derived from the Receiver Operating Characteristic (ROC) is used for threshold design. The considered methodology requires the knowledge of the involved subsea production system, in particular of its hotspots whose failure could cause an oil spill

Wireless Sensor Networks for Detection and Localization of Subsea Oil Leakages

This work studies the impact of Wireless Sensor Networks (WSNs) for oil spill detection and localization in Subsea Production Systems. The case study is the Goliat FPSO, with a realistic assumption about the presence of a WSN built upon the existing passive acoustic sensors installed on each subsea template to monitor the manifold. The sensors take local binary decisions regarding the presence/absence of a spill by performing an energy test. A Fusion Center (FC) collects such local decisions and provides a more reliable global binary decision. The Counting Rule (CR) and a modified Chair-Varshney Rule (MCVR) are compared. An objective function based on the Receiver Operating Characteristic (ROC) is used for threshold design. The FC, in case of a spill detection, provides an estimated position of the leak source. Four localization algorithms are explored: Maximum A-Posteriori (MAP) estimation, Minimum Mean Square Error (MMSE) estimation, and two heuristic centroid-based algorithms. Detection and localization performances are assessed in comparison to the (position) Clairvoyant Chair-Varshney Rule (CVR) and to the Cramér-Rao Lower Bound (CRLB), respectively. The considered framework requires the prior knowledge of the involved subsea production system in term

Spatio-Temporal Decision Fusion for Quickest Fault Detection Within Industrial Plants: The Oil and Gas Scenario

In this work, we present a spatio-temporal decision fusion approach aimed at performing quickest detection of faults within an Oil and Gas subsea production system. Specifically, a sensor network collectively monitors the state of different pieces of equipment and reports the collected decisions to a fusion center. Therein, a spatial aggregation is performed and a global decision is taken. Such decisions are then aggregated in time by a post-processing center, which performs quickest detection of system fault according to a Bayesian criterion which exploits change-time statistical distributions originated by system components’ datasheets. The performance of our approach is analyzed in terms of both detection- and reliability-focused metrics, with a focus on (fast & inspection-cost-limited) leak detection in a real-world oil platform located in the Barents Sea

Subsea Oil Spill Risk Management Based on Sensor Networks

The use of Wireless Sensor Networks (WSNs) in support of Dynamic Risk Assessment regarding oil spills still lacks a proper integration. WSNs enable prompt responses to such emergencies through an appropriate inspection, thus avoiding possible larger disasters. This work proposes a methodology for the setup of a WSN as a Leak Detection System in which a Fusion Center collects sensors’ binary decisions and provides a more reliable decision about the presence/absence of a leak. The detection rules are based on statistical signal processing techniques, and the choice of the optimal thresholds is made through the optimization of three objective functions tailored to the Oil&Gas industry. Detection performances are assessed in terms of the Receiver Operating Characteristic (ROC) curve. The case study is the Goliat FPSO, a production platform located in the Barents Sea, and related requirements dictated by Norwegian authorities to prevent oil spills. The considered WSN monitors the subsea manifolds through passive acoustic sensors