Murat Yasar

Review and comparative evaluation of symbolic dynamic filtering for detection of anomaly patterns

Symbolic dynamic filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a review of SDF and its performance evaluation relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of: (i) anomaly detection capability, (ii) decision making for failure mitigation and (iii) computational efficiency. The evaluation is based on analysis of time series data generated from a nonlinear active electronic system.

Aircraft Accident Prevention: Loss-of-Control Analysis

The majority of fatal aircraft accidents are associated with ‘loss-of-control’. Yet the notion of loss-of-control is not well-deflned in terms suitable for rigorous control systems analysis. Loss-of-control is generally associated with ∞ight outside of the normal ∞ight envelope, with nonlinear in∞uences, and with an inability of the pilot to control the aircraft. The two primary sources of nonlinearity are the intrinsic nonlinear dynamics of the aircraft and the state and control constraints within which the aircraft must operate. In this paper we examine how these nonlinearities afiect the ability to control the aircraft and how they may contribute to loss-of-control. Examples are provided using NASA’s Generic Transport Model.

Sensor fusion for fault detection and classification in distributed physical processes

This paper proposes a feature extraction and fusion methodology to perform fault detection & classification in distributed physical processes generating heterogeneous data. The underlying concept is built upon a semantic framework for multi-sensor data interpretation using graphical models of Probabilistic Finite State Automata (PFSA).While the computational complexity is reduced by pruning the fused graphical model using an information-theoretic approach, the algorithms are developed to achieve high reliability via retaining the essential spatiotemporal characteristics of the physical processes. The concept has been validated on a simulation test bed of distributed shipboard auxiliary systems.

Anomaly detection for health management of aircraft gas turbine engines

This paper presents a comparison of different pattern recognition algorithms to identify slow time scale anomalies for health management of aircraft gas turbine engines. A new tool of anomaly detection, based on symbolic dynamics and information theory, is compared with traditional pattern recognition tools of principal component analysis (PCA) and artificial neural network (ANN). Time series data of the observed variables on the fast time scale are analyzed at slow time scale epochs for early detection of anomalies. The time series data are obtained from a generic engine simulation model. Health monitoring of gas turbine engines based on these techniques is discussed.

Integrated Electric Power System supervision for reconfiguration and damage mitigation

Autonomous shipboard electric power system operation is one of the major goals in order to ensure safe, reliable and economic system operation. Integrated electrical power systems need to be capable of redistributing power almost instantly in order to supply both non-propulsion and propulsion shipboard electrical loads. The dynamics of the power systems require differential-algebraic equations along with discrete switching actions to account for load shedding and redistribution. Therefore, the reconfiguration problem can be formulated in a hybrid supervision framework in order to have a complete system description. The supervisory system will also be required to accommodate faults and failures to achieve damage mitigation. The paper discusses an approach of hybrid supervisory reconfiguration and damage mitigation in the context of a novel architecture of shipboard electric distribution systems.

Hierarchical Control of Rotorcraft for Enhanced Performance and Structural Durability

This paper presents a hierarchical control law architecture for future generation rotorcraft for enhanced performance (i.e., handling qualities) and structural durability. The proposed control system has a two-tier hierarchical architecture. The lower-tier is designed using a combination of probabilistic robust control and damage mitigating control methodologies. By allowing different levels of risk under different flight conditions, probabilistic robust control achieves the desired trade off between stability, robustness and nominal performance. Minimization of damage rate is achieved via damage mitigating control, improving health management and durability of the rotorcraft. The upper-tier is designed using discrete-event supervisory control methodology, which monitors the system response for any anomalous behavior, performance degradation and/or potential loss of structural durability. Based on the observed data, the upper-tier supervisor may decide to switch between different modes to satisfy the specified requirements. The system is demonstrated using a high fidelity simulation of the UH-60A helicopter.

Fault diagnosis and isolation in aircraft gas turbine engines

This paper formulates and validates a novel methodology for diagnosis and isolation of incipient faults in aircraft gas turbine engines. In addition to abrupt large faults, the proposed method is capable of detecting and isolating slowly evolving anomalies (i.e., deviations from the nominal behavior), based on analysis of time series data observed from the instrumentation in engine components. The fault diagnosis and isolation (FDT) algorithm is based upon Symbolic Dynamic Filtering (SDF) that has been recently reported in literature and relies on the principles of Symbolic Dynamics, Statistical Pattern Recognition and Information Theory. Validation of the concept is presented and a real life software architecture is proposed based on the simulation model of a generic two-spool turbofan engine for diagnosis and isolation of incipient faults.

A comprehensive control strategy for integrated flight/propulsion systems:

Abstract Integration of flight and propulsion control systems in advanced aircraft has attracted much attention because of ever increasing demand on enhancement of performance and reliability. As the underlying dynamic couplings and non-linear interactions of flight and propulsion systems are too complex for realtime execution in onboard computational platforms, hierarchical hybrid (i.e. combined continuously varying and discrete event) architecture is proposed for the development of future generation control systems that will take the advantage of these interactions for mission enhancement. Although the original structures of continuously varying control systems for propulsion and flight are retained, discrete event supervisory (DES) control would facilitate decision-making for aircraft operation. DES decisions regarding propulsion and flight control influence the performance and reliability of the entire vehicle control system due to interactions at the level of continuously varying dynamics. A two-level hierarchical DES control system is designed to supervise and coordinate the operation of twin-engine aircraft propulsion with flight dynamics. In essence, the propulsion system is integrated with the flight dynamical system such that the DES controller at the propulsion level of hierarchy provides load balancing of the engines as well as overall health and mission management of the aircraft propulsion system. The parameter-scheduling dynamic-inversion controller stabilizes and drives the flight system in the vehicle operation envelope and compensates for potential unbalance and any other undesirable action, resulting from discrete event supervision of the propulsion system. Results of real-time simulation on a test bed are presented to demonstrate the efficacy of the proposed control concept.

A Simulation Test Bed for Coordination of Unmanned Rotorcraft and Ground Vehicles

This paper presents the development of a simulation test bed for a command, control, computer, communication, intelligence, surveillance and reconnaissance (C 4 ISR) system. The test bed features coordination of a rotorcraft unmanned aerial vehicle (RUAV) and multiple unmanned ground vehicles (UGVs) by integrating a high-fidelity rotorcraft model with actual ground-based robots that emulate UGVs. The RUAV component of the test bed is realized on a networked computer simulation, whereas networked robotics hardware is employed for the UGV representation. The RUAV dynamics run a high-fidelity nonlinear simulation model of a UH-60A Black Hawk helicopter. To emulate the dynamics of the UGVs, several networked Segway robots are employed. The proposed C 4 ISR system undertakes the task of mission management from the top level discrete-event supervision (DES) to the bottom level continuous-time regulation.

Hierarchical Discrete Event Supervisory Control of Aircraft Propulsion Systems

Abstract : This paper presents a hierarchical application of Discrete Event Supervisory (DES) control theory for intelligent decision and control of a twin-engine aircraft propulsion system. A dual layer hierarchical DES controller is designed to supervise and coordinate the operation of two engines of the propulsion system. The two engines are individually controlled to achieve enhanced performance and reliability, necessary for fulfilling the mission objectives. Each engine is operated under a continuously varying control system that maintains the specified performance and a local discrete-event supervisor for condition monitoring and life extending control. A global upper level DES controller is designed for load balancing and overall health management of the propulsion system.