John D. Hios

FDI for Aircraft Systems Using Stochastic Pooled-NARMAX Representations: Design and Assessment

In this paper, two statistical schemes aiming at effective fault detection and isolation (FDI) for aircraft systems are introduced. They are based on novel stochastic pooled nonlinear autoregressive moving average with exogenous excitation representations that model the relationships among available aircraft signals, as well as statistical decision making. The first, or ldquodirect,rdquo scheme relates a pilot input to a measurable flight attitude via a two-stage pooled representation. The second, or ldquoindirect,rdquo scheme relates four attitude-dependent flight variables via a proper pooled representation. Both schemes achieve effective FDI operation inside an entire flight regime, under stochastic effects and uncertainty and under various operating or environmental conditions, at the price of increased computational effort during training. Their performance and robustness are assessed via many flights conducted with an aircraft simulator inside the considered flight regime, under different conditions and under faults of various types and magnitudes.

Statistical Damage Detection in a Smart Structure under Different Temperatures via Vibration Testing: A Global Model Based Approach

Statistical damage detection in a structure operating under different temperatures via vibration testing is addressed by means of a stochastic global model based approach. The approach relies upon novel global models of the Functionally Pooled (FP) form, which are capable of describing the dynamics under any temperature, and statistical decision making. In its present form the approach utilizes response (output–only) vibration data, although excitation–response data may be also used. Its effectiveness is confirmed via a large number of experiments performed on a smart composite beam under different temperatures within the [¡20; +20]oC range.

Aircraft fault detection and identification by stochastic functionally pooled modelling of relationships among attitude data

Abstract This paper introduces a statistical fault detection and identification (FDI) scheme for aircraft systems, which uses flight attitude data rather than information from purposely developed physical or virtual sensors. The scheme is based on the modelling of relationships among the considered data via stochastic Time-dependent Functionally Pooled Non-linear AutoRegressive with Exogenous excitation (TFP-NARX) representations. These are globally valid inside a flight regime and under various considered environmental conditions, thanks to the pooling technique used for their identification. Moreover, due to the TFP-NARX coefficients being a function of time-dependent quantities, high modelling accuracy is achieved. The schemes operation involves identifying nominal TFP-NARX models of relationships among the attitude data from an aircraft operating in a healthy state. Owing to fault occurrence, these relationships may change. Then, an in-flight comparison of the nominal and the current aircraft dynamics provides fault-related information, which is statistically evaluated for FDI purposes. The schemes performance and robustness are assessed with numerous flights conducted throughout a flight regime under various manoeuvring settings and turbulence levels.

Stochastic Identification Under Multiple Operating Conditions: Functionally Pooled VARMA Methods

Abstract The identification of stochastic systems capable of operating under different conditions is addressed based on data records corresponding to each condition. The problem is important in various applications, and is tackled within a recently introduced, novel, Functional Pooling framework. The study focuses on the identification of Functionally Pooled Vector AutoRegressive Moving Average (FP–VARMA) models within this framework. Two–Stage Least Squares and Maximum Likelihood estimators are formulated, while model structure selection is postulated via a canonical correlation analysis scheme and information criteria. The performance characteristics of the identification approach are assessed via a Monte Carlo study.

Fault Detection and Isolation in Aircraft Systems Using Stochastic Nonlinear Modelling of Flight Data Dependencies

This paper introduces a fault detection and isolation (FDI) scheme for aircraft systems based on the modelling of the relationships among flight variables. The modelling is performed by means of pooled nonlinear autoregressive with exogenous (NARX) excitation representations. During the systems operation in healthy mode, these relationships are valid. Hence, a scheme using statistical hypothesis testing is designed to detect changes in these relationships as a result of fault occurrence. The FDI schemes performance and robustness are assessed with flights conducted under various external flight conditions (turbulence)

On-board statistical detection and control of anomalous pilot-aircraft interactions

Abstract A statistical method for the on-board detection and control of oscillatory phenomena in pilot-aircraft systems is presented. Recursive identification is used to obtain a linear model of the system at every time instant. The estimated system parameters are monitored, and the system stability margins are continuously assessed. Oscillations due to stability loss are detected early using a composite statistical hypothesis test. Finally a simple stability augmentation system is designed to assist the pilot-aircraft system during the critical time intervals. The method is successfully tested with data from a detailed nonlinear aircraft model and a flight simulator facility.

Integral minimum variance-like control for pooled non-linear representations with application to an aircraft system

This paper presents an integral minimum variance-like controller design based upon a constant coefficient pooled non-linear autoregressive moving average with exogenous excitation (CCP-NARMAX) representation. The use of pooling techniques significantly enhances the NARMAX representations ability to accurately describe systems performing under various operating conditions such as aircraft systems, chemical processes, industrial systems and so on. The controller design introduces suitable modifications to account for the characteristics of the CCP-NARMAX representation. The control strategy is subsequently applied to a non-linear aircraft system in order to obtain regulation of the pitch rate around a predetermined value. Comparisons with a conventional PID control design are also made under various operating conditions, including disturbances due to external input and turbulence.

On-board statistical detection and compensation of anomalous pilot-aircraft interactions

A statistical method for the on-board detection and compensation of adverse oscillations in pilot-aircraft systems is presented. A recursively updated linear model representing the pilot-aircraft system is used to continuously check for stability loss. The instability-related oscillations are detected early, using a statistical hypothesis test. Furthermore, a specially designed stability augmentation system assists the pilot during the instability periods. The methods effectiveness is demonstrated via data obtained from a flight simulator and a detailed simulation model.

Stochastic Vector Identification and Uncertain Modal Parameter Estimation for a Smart Composite Beam

This study aims at identifying the modal characteristics and their uncertainties for a smart composite beam. The problem is addressed via Vector AutoRegressive with eXogenous excitation (VARX) models. The advantages of VARX modeling include simplicity of implementation, high accuracy, parsimony of representation, and capability of handling modal uncertainties. Two different approaches to assess the modal parameter uncertainties are investigated. The first is based upon linearizing the function that relates the VARX model parameters with the modal parameters, whereas the second is based upon computer simulations using the Monte Carlo and the bootstrap schemes. The results indicate that VARX modeling captures the system dynamics and provides accurate modal parameters with tight confidence intervals.

Nonlinear Integral Minimum Variance-Like Control with Application to an Aircraft System

This paper presents an integral minimum variance-like controller design based upon a specific pooled non-linear autoregressive moving average with exogenous excitation (NARMAX) representation. The controller design introduces suitable modifications to account for the characteristics of the pooled NARMAX representation. The control strategy is subsequently applied to a nonlinear aircraft system in order to obtain regulation of the pitch rate around a predetermined value (zero for the given case). Comparisons with a conventional PID control design are also made under various operating conditions (disturbances due to external input/turbulence)