Devendra Tolani

Anomaly Detection in Aircraft Gas Turbine Engines

ERFORMANCE,reliability,andoperationalflexibilityarecriticalrequirementsofcomplexmechanicalsystems,suchasaircraftpropulsion,andtheserequirementsmayvarywithdifferentmissionperspectivesduringtheservicelife. In spite of meticulous engineering design, complex systems do eventually degrade and often fail to yield theanticipatedperformanceduringthelaterphasesoftheiroperationallife.Recently,muchattentionhasbeendevotedtocondition-based maintenance using the online information of sensor-based observables. For example, life extendingcontrol

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.

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.

Reliable operation of rotorcraft using probabilistic robust control

Abstract A hierarchical architecture is presented for the design of reliable control systems for high performance rotorcraft. The low-level controller is designed using probabilistic robust control approaches. By allowing different levels of risk under different flight conditions, the control system achieved the desired trade off between stability robustness and nominal performance. A high-level supervisory controller is proposed to monitor system response and switch between low-level robust controllers with different levels of risk and perfonnance.

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.

Prognosis of Failure Precursor in Complex Electrical Systems Using Symbolic Dynamics

Failures in a plants electrical components are a major source of performance degradation and plant unavailability. In order to detect and monitor failure precursors and anomalies early in electrical systems, we have developed signal processing capabilities that can detect and map patterns in already existing and available signals to an anomaly measure. Toward this end, the language measure theory based on real analysis, finite state automaton, symbolic dynamics and information theory has been deployed. Application of this theory for electronic circuit failure precursor detection resulted in a robust statistical pattern recognition technique. This technique was observed to be superior to conventional pattern recognition techniques such as neural networks and principal component analysis for anomaly detection because it exploits a common physical fact underling most anomalies which conventional techniques do not. Symbolic dynamic technique resulted in a monotonically increasing smooth anomaly plot which was experimentally repeatable to a remarkable accuracy. For the Van der Pol oscillator circuit board experiment, this lead to consistently accurate predictions for the anomaly parameter and its range.

Integrated decision and control of human-engineered complex systems

This paper presents a comprehensive decision and control strategy for human-engineered complex systems to achieve simultaneously the following objectives: (i) high-performance with quality assurance; (ii) reliability and structural durability with extended service life and (iii) operability over a wide range. Results from several systems-theoretic disciplines, such as probabilistic robust control (PRC), damage mitigating control (DMC), health and usage monitoring (HUM) and discrete event supervisory (DES) decision and control have been synergistically combined to achieve the above goal. The proposed decision and control system is hierarchically structured with two-tier architecture. The lower tier incorporates continuously-varying control that is designed using a combination of PRC and DMC, and the upper tier is designed to provide information and intelligence through DES decision and control that monitors the system response for detection and mitigation of anomalous behaviour, performance degradation and potential degradation of structural durability. To assure desired quality at permissible levels of risk as well as under different operating conditions, the PRC at the lower tier makes a trade off between robustness and performance, while damage mitigation in critical structures is achieved via DMC that also facilitates health and usage monitoring of the complex system. Based on the information derived from the observed time series data, the DES decision and control at the upper tier may decide to switch, in real time, to one control module from another in order to satisfy the specified performance and safety requirements. The switching actions are executed at the lower tier. The integrated system, including the proposed decision and control architecture, has been tested and validated on a rotorcraft simulation test bed.

Optimal supervisory control of aircraft propulsion: a discrete event approach

This paper presents an 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 optimally designed for load balancing and overall health and mission management of the aircraft propulsion system.