Final Report of the DAUFIN project

DAUFIN = Data Assimulation within Unifying Framework for Improved river basiN modeling (EC 5th framework Project

Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available

Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated

Report of the Workshop on Survey Design and Data Analysis (WKSAD) [21- 25 June, 2004, Aberdeen, UK]

Contributors: Knut Korsbrekke, Michael Penningto

Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational Awareness

Determining the time, location, and severity of natural disaster impacts is fundamental to formulating mitigation strategies, appropriate and timely responses, and robust recovery plans. A Landslide Hazard Assessment for Situational Awareness (LHASA) model was developed to indicate potential landslide activity in near real-time. LHASA combines satellite-based precipitation estimates with a landslide susceptibility map derived from information on slope, geology, road networks, fault zones, and forest loss. Precipitation data from the Global Precipitation Measurement (GPM) mission are used to identify rainfall conditions from the past seven days. When rainfall is considered to be extreme and susceptibility values are moderate to very high, a nowcast is issued to indicate the times and places where landslides are more probable. When LHASA nowcasts were evaluated with a Global Landslide Catalog, the probability of detection (POD) ranged from 8 to 60%, depending on the evaluation period, precipitation product used, and the size of the spatial and temporal window considered around each landslide point. Applications of the LHASA system are also discussed, including how LHASA is used to estimate long-term trends in potential landslide activity at a nearly global scale and how it can be used as a tool to support disaster risk assessment. LHASA is intended to provide situational awareness of landslide hazards in near real-time, providing a flexible, open source framework that can be adapted to other spatial and temporal scales based on data availability

Monitoring and mitigation of the sound effects of hydrocarbon exploration activities on marine mammal populations

Offshore Exploration and Production (E&P) activities, such as seismic surveys and drilling, generate sound that can affect marine mammals in different ways. These effects range from permanent or temporary auditory impacts to disturbance or behavioral changes, and communication masking. Depending on the intensity and duration of these effects, and without implementation of appropriate mitigation measures, this can result in population-level consequences. The overarching objective of this study was to advance the protection of marine mammals during the implementation of E&P activities through the following themes: (1) enhancement of the state of knowledge of risk management, (2) efficacy of mitigation, (3) advanced monitoring technology, (4) implementation of advanced industry monitoring and mitigation measures and (5) measurement of heretofore unassessed E&P activities. In this study several marine mammal monitoring and mitigation programs associated with E&P projects are presented to further advance these themes. Topics being addressed include the use of autonomous camera systems for aerial monitoring of a narwhal population, long-term photo-identification studies of western gray whales to better understand site fidelity to their summer feeding grounds, mitigation of gray whales’ behavioral responses to a seismic survey near these feeding grounds and use of Passive Acoustic Monitoring to characterize seismic pulses and drilling activity as well as marine mammal presence in remote arctic areas. A synthesis of the main findings is provided that includes identification of future research needs. Conclusions and specific recommendations are made that will contribute to our ability to assess and mitigate risks of E&P sound to marine mammals