Analysis, estimation and control for perturbed and singular systems for systems subject to discrete events.

"The principle investigator for this effort is Professor Alan S. Willsky, and Professor George C. Verghese is co-principal investigator."--P. [3].Includes bibliographical references (p. [20]-[25]).Final technical report for grant AFOSR-88-0032.Supported by the AFOSR. AFOSR-88-003

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements

Benelux meeting on systems and control, 23rd, March 17-19, 2004, Helvoirt, The Netherlands

Book of abstract

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Fault diagnosis of hybrid systems with applications to gas turbine engines

Stringent reliability and maintainability requirements for modern complex systems demand the development of systematic methods for fault detection and isolation. Many of such complex systems can be modeled as hybrid automata. In this thesis, a novel framework for fault diagnosis of hybrid automata is presented. Generally, in a hybrid system, two types of sensors may be available, namely: continuous sensors supplying continuous-time readings (i.e., real numbers) and threshold sensitive (discrete) sensors supplying discrete outputs (e.g., level high and pressure low). It is assumed that a bank of residual generators (detection filters) designed based on the continuous model of the plant is available. In the proposed framework, each residual generator is modeled by a Discrete-Event System (DES). Then, these DES models are integrated with the DES model of the hybrid system to build an Extended DES model. A "hybrid" diagnoser is then constructed based on the extended DES model. The "hybrid" diagnoser effectively combines the readings of discrete sensors and the information supplied by residual generators (which is based on continuous sensors) to determine the health status of the hybrid system. The problem of diagnosability of failure modes in hybrid automata is also studied here. A notion of failure diagnosability in hybrid automata is introduced and it is shown that for the diagnosability of a failure mode in a hybrid automaton, it is sufficient that the failure mode be diagnosable in the extended DES model developed for representing the hybrid automaton and residual generators. The diagnosability of failure modes in the case that some residual generators produce unreliable outputs in the form of false alarm or false silence signals is also investigated. Moreover, the problem of isolator (residual generator) selection is examined and approaches are developed for computing a minimal set of isolators to ensure the diagnosability of failure modes. The proposed hybrid diagnosis approach is employed for investigating faults in the fuel supply system and the nozzle actuator of a single-spool turbojet engine with an afterburner. A hybrid automaton model is obtained for the engine. A bank of residual generators is also designed, and an extended DES is constructed for the engine. Based on the extended DES model, a hybrid diagnoser is constructed and developed. The faults diagnosable by a purely DES diagnoser or by methods based on residual generators alone are also diagnosable by the hybrid diagnoser. Moreover, we have shown that there are faults (or groups of faults) in the fuel supply system and the nozzle actuator that can be isolated neither by a purely DES diagnoser nor by methods based on residual generators alone. However, these faults (or groups of faults) can be isolated if the hybrid diagnoser is used

Development and analysis of a homogeneous long-term precipitation network (1850-2015) and assessment of historic droughts for the island of Ireland

Long-term precipitation series are critical for understanding emerging changes to the hydrological cycle. Given the paucity of long-term quality assured precipitation records in Ireland this thesis expands the existing catalogue of long-term monthly precipitation records for the Island by recovering and digitising archived data. Following bridging and updating, 25 stations are quality assured and homogenised using state-of-the-art methods and scrutiny of station metadata. Assessment of variability and change in the homogenised and extended precipitation records for the period 1850-2010 reveals positive (winter) and negative (summer) trends. Trends in records covering the typical period of digitisation (1941 onwards) are not always representative of longer records. Using this quality assured network of precipitation stations together with proxy rainfall reconstructions a 250-year historic drought catalogue is established using the Standardised Precipitation Index (SPI). Documentary sources, particularly newspaper archives, spanning the last 250 years are used to (i) add confidence to the quantitative detection of drought episodes and (ii) gain insight to the socio-economic impacts of historic droughts. During the years 1850-2015 seven major drought rich periods with an island wide fingerprint are identified in 1854-1860, 1884-1896, 1904-1912, 1921-1924, 1932-1935, 1952-1954 and 1969-1977. These events exhibit substantial diversity in terms of drought development, severity and spatial occurrence. Results show that Ireland is drought prone but recent decades are unrepresentative of the longer-term drought climatology. Finally, long-term homogenous precipitation records are further utilised to reconstruct river flows at twelve study catchments to 1850. Reconstructed flows are analysed to identify periods of hydrological drought and the potential of different SPI accumulations to forecast severe drought are explored. Results demonstrate the importance of catchment characteristics in moderating the effects of meteorological drought and highlight the potential for drought forecasting in groundwater dominated catchments. The body of work presented considerably advances understanding of the long-term hydro-climatology of a sentinel location in Europe and provides datasets and tools for more resilient water management