Exogenous Excitation Research Articles

Aircraft engine health management via stochastic modelling of flight data interrelations

A novel engine health management (EHM) scheme is introduced. It uses flight-level, instead of thermodynamic, data to cost-effectively augment the onboard EHM redundancy. For a nominal healthy aircraft, fault-sensitive interrelations among flight data are globally modelled inside a flight regime via Constant-Coefficient Pooled Nonlinear AutoRegressive with eXogenous (CCP-NARX) excitation representations. Single or sequential engine faults perturb these interrelations. Statistically evaluating the perturbation-induced effects draws reliable conclusions on the engineʼs health. Validation and comparisons with Kalman filter-based alternatives are made throughout the regime under various operational conditions.

Comparison of human optimized bacterial luciferase, firefly luciferase, and green fluorescent protein for continuous imaging of cell culture and animal models

Bioluminescent and fluorescent reporter systems have enabled the rapid and continued growth of the optical imaging field over the last two decades. Of particular interest has been noninvasive signal detection from mammalian tissues under both cell culture and whole animal settings. Here we report on the advantages and limitations of imaging using a recently introduced bacterial luciferase (lux) reporter system engineered for increased bioluminescent expression in the mammalian cellular environment. Comparison with the bioluminescent firefly luciferase (Luc) system and green fluorescent protein system under cell culture conditions demonstrated a reduced average radiance, but maintained a more constant level of bioluminescent output without the need for substrate addition or exogenous excitation to elicit the production of signal. Comparison with the Luc system following subcutaneous and intraperitoneal injection into nude mice hosts demonstrated the ability to obtain similar detection patterns with in vitro experiments at cell population sizes above 2.5 × 10(4) cells but at the cost of increasing overall image integration time.

Model updating of multistory shear buildings for simultaneous identification of mass, stiffness and damping matrices using two different soft-computing methods

In this research, two novel methods for simultaneous identification of mass–damping–stiffness of shear buildings are proposed. The first method presents a procedure to estimate the natural frequencies, modal damping ratios, and modal shapes of shear buildings from their forced vibration responses. To estimate the coefficient matrices of a state-space model, an auto-regressive exogenous excitation (ARX) model cooperating with a neural network concept is employed. The modal parameters of the structure are then evaluated from the eigenparameters of the coefficient matrix of the model. Finally, modal parameters are used to identify the physical/structural (i.e., mass, damping, and stiffness) matrices of the structure. In the second method, a direct strategy of physical/structural identification is developed from the dynamic responses of the structure without any eigenvalue analysis or optimization processes that are usually necessary in inverse problems. This method modifies the governing equations of motion based on relative responses of consecutive stories such that the new set of equations can be implemented in a cluster of artificial neural networks. The number of neural networks is equal to the number of degree-of-freedom of the structure. It is shown the noise effects may partially be eliminated by using high-order finite impulse response (FIR) filters in both methods. Finally, the feasibility and accuracy of the presented model updating methods are examined through numerical studies on multistory shear buildings using the simulated records with various noise levels. The excellent agreement of the obtained results with those of the finite element models shows the feasibility of the proposed methods.

Active fault diagnosis by controller modification

Two active fault diagnosis methods for additive or parametric faults are proposed. Both methods are based on controller reconfiguration rather than on requiring an exogenous excitation signal, as it is otherwise common in active fault diagnosis. For the first method, it is assumed that the system considered is controlled by an observer-based controller. The method is then based on a number of alternate observers, each designed to be sensitive to one or more additive faults. Periodically, the observer part of the controller is changed into the sequence of fault sensitive observers. This is done in a way that guarantees the continuity of transition and global stability using a recent result on observer parameterization. An illustrative example inspired by a field study of a drag racing vehicle is given. For the second method, an active fault diagnosis method for parametric faults is proposed. The method periodically adds a term to the controller that for a short period of time renders the system unstable if a fault has occurred, which facilitates rapid fault detection. An illustrative example is given.

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.

AN OBSERVER PARAMETERIZATION APPROACH TO ACTIVE FAULT DIAGNOSIS WITH APPLICATIONS TO A DRAG RACING VEHICLE

An active fault diagnosis method for additive, parametric or multiplicative faults is proposed. It is assumed that the system considered is controlled by an observer based controller. The method is then based on a number of alternate observers, each designed to be sensitive to one or more faults. Periodically, the observer part of the controller is changed into the sequence of fault sensitive observers. This is done in a way that guarantees continuity of transition and global stability using a recent result on observer parameterization. The proposed active fault diagnosis method distinguishes itself from the existing literature in terms of not relying on an exogenous excitation signal. An illustrative example based on a drag racing vehicle is given.

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

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 scheme's 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 scheme's performance and robustness are assessed with numerous flights conducted throughout a flight regime under various manoeuvring settings and turbulence levels.

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 representation's 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.

ARMAX modal parameter identification in the presence of unmeasured excitation—I: Theoretical background

This study investigates the performance of a time-domain parameter estimation algorithm aimed at identifying modal parameters from excitation and response data corrupted with significant measurement noise and unmeasured sources of periodic and random excitation. The parameters of an autoregressive moving average with exogenous excitation (ARMAX) model are estimated using an iterative multistage estimation algorithm. The use of backwards autoregressive with exogenous excitation (ARX) models in the multistage algorithm allows vibrational modes to be distinguished from spurious numerical poles and is also the basis of a model selection criterion. A diagonal parameterisation of the autoregressive (AR) polynomial matrices allows the MIMO ARMAX model to be separated into a number of MISO systems, and permits simple manipulation and stabilisation of the estimated model. Measurement noise and sources of unmeasured random and periodic excitations are accounted for by the ARMAX model structure. In this paper, the theory and algorithm of the ARMAX model is given.

ARMAX modal parameter identification in the presence of unmeasured excitation—II: Numerical and experimental verification

The performance of an autoregressive moving average with exogenous excitation (ARMAX) time-domain parameter estimation algorithm is assessed using data simulating the response of a two degree-of-freedom system and also experimental data obtained from a cantilever aluminium beam. The algorithm is aimed at identifying modal parameters in the presence of significant measurement noise and unmeasured sources of periodic and random excitation. The simulated system exhibits lightly damped modes and is excited with measured random excitation as well as unmeasured random and periodic excitation. A second case is considered where the modal frequencies of the simulated system are separated by 0.345 Hz and the frequency of an unmeasured periodic excitation is 0.257 Hz below the first modal frequency. Electrodynamic shakers as well as piezoceramic plates are used to apply multiple independent excitation forces, including measured random noise and also unmeasured random and periodic signals. Successful identification of modal parameters is achieved in the presence of up to 10% random measurement noise, as well as over 200% unmeasured periodic and random excitations. Results showed that the ARMAX algorithm achieved better results than ARX models estimated from experimental data corrupted by the effects of unmeasured excitations. The use of piezoceramic plates for structural excitation was demonstrated in experiments and the ability to excite modes was observed to be dependent on the deflection of a particular mode in the contact area of the piezoceramic plates.

Prediction error method-based second-order structural identification algorithm in stochastic state space formulation

This paper presents a time-domain prediction error method (PEM)-based system identification method for the identification of second-order structural parameters of linear structural systems. The state space model of a linear structure is first expanded into linear AutoRegressive Moving Average with eXogenous excitation (ARMAX) or AutoRegressive Moving Average (ARMA) models by matrix fraction description. Second-order structural parameters are then estimated directly from measured time series data by applying the PEM to ARMAX and ARMA models. The proposed structural identification method can incorporate as much as possible structural information known a priori into the structural identification process to improve the identification accuracy. To avoid the practical difficulty often associated with input measurement, a two-stage structural health monitoring procedure is proposed. Once the mass matrix is identified from the first stage, data collected from ambient vibration survey can be used to identify other structural parameters in the second stage. The effectiveness of the proposed structural identification method as well as the corresponding structural health monitoring method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The numerical results show that the proposed method is capable of locating and quantifying damages reasonably well in the presence of 10% measurement noise and limited sensor information. Copyright © 2006 John Wiley & Sons, Ltd.

On-line identification of operational loads using exogenous inputs

When the FRF matrix describing the dynamical behavior of a structure is available, the operational loads can be determined by multiplying the pseudo-inverse of the FRF matrix by the operational responses (displacements, velocities or accelerations). In practice, however, the boundary conditions of the structure in operation deviate from the ones in laboratory conditions (due to e.g. aerodynamic loads, fuel consumption, temperature changes). This means that measurements during operation should be taken in order to obtain the correct FRF matrix. Unfortunately, it is not always possible to measure all operational loads acting on the structure (which is needed to calculate the FRFs). In this paper, a method is proposed that enables the on-line determination of operational forces. As input the method uses dynamical response measurements and the measurement of a known force (due to an exogenous excitation input) at one particular location (where it is possible to put an excitation device and a force sensor). A periodic signal is taken as the exogenous excitation. It is assumed that apart from the known force there is also an unknown force (at an unknown location) that is acting on the structure. As a first step in the procedure, the measured responses and the known (i.e. measured) force are compensated in order to eliminate the contribution due to the unknown force. From these compensated measurements the complete FRF matrix is calculated. Then, the forces are calculated from the original (uncompensated) responses and the inverted complete FRF matrix. The method is validated both on a simulation and measurements of a steel beam with an applied unknown impact excitation.

Bioluminescence resonance energy transfer assays for protein-protein interactions in living cells.

We recently developed a bioluminescence resonance energy transfer (BRET) system for assaying protein-protein interactions (,), which has been used successfully for studying the interaction of circadian clock proteins isolated from cyanobacteria, and tested in Escherichia coli cells (), and the dimerization of human β2-adrenergic receptors in mammalian cells (). BRET results from the nonradiative energy transfer between a donor and an acceptor. It is related to fluorescence resonance energy transfer (FRET) (,), except that, in FRET, there are two fluorophores, one that absorbs exogenous excitation (the donor) and passes the energy to the other fluorophore (the acceptor); in BRET, the donor is a luciferase that generates its own luminescence emission in the presence of a substrate, and can pass the energy to an acceptor fluorophore. For either BRET or FRET to work, the donor’s emission spectrum must overlap the acceptor’s absorption spectrum, their transition dipoles must be in an appropriate orientation, and the donor and acceptor must be in close proximity (usually within 30-80 Å of each other, depending on the degree of spectral overlap) (). During a BRET assay for molecular interactions, the first criterion is fixed for a given donor-acceptor combination, but the relative orientation and distance between the donor and acceptor will change depending on the strength of the interaction. Although its use as a protein interaction assay is novel, BRET is a natural phenomenon.

MIMO LMS-ARMAX IDENTIFICATION OF VIBRATING STRUCTURES—PART I: THE METHOD

A comprehensive linear multi stage autoregressive moving average with exogenous excitation (LMS-ARMAX) method for effective multiple-input multiple-output (MIMO) structural dynamics identification in the presence of noise is introduced. The method consists of (a) a vector ARMAX representation of an appropriate form, (b) effective LMS parameter estimation, (c) statistical order selection/validation, and (d) a digital dispersion analysis (DA) methodology for effective modal characterization. The LMS-ARMAX method overcomes many of the difficulties that had rendered MIMO ARMAX identification difficult in the past, featuring modest computational complexity, high accuracy, guaranteed algorithmic and model stability, and thus applicability to higher-dimensional problems and lightly damped structures, accurate modal parameter extraction, and effective distinction of structural from ‘extraneous’ modes. A critical assessment of the LMS-ARMAX method under various noise conditions, as well as comparisons with a simpler ARX version and the ERA (Eigensystem Realization Algorithm), are undertaken based upon experimental vibration data obtained from a scale aircraft skeleton structure. The paper is divided into two parts: The LMS-ARMAX method is presented in the first, and its critical assessment and comparisons in the second.

Excitation Versus Control Issues in Closed Loop Identification of Plant Models for a Sugar Cane Crushing Mill

This paper addresses the selection of excitation signals to enhance the identification of a linear model of a plant system from data collected whilst the plant is operating under closed loop control. Theoretical results are developed which characterize the effective frequency weighting induced by the closed loop to the modelling criterion. These results then are used to establish the cause of ‘identification of the controller’ and to study mechanisms for influencing the model fit to focus on the plant system through the selection of exogenous excitation signals introduced to the loop. Experimental results of closed loop identification with and without excitation on the plant inputs are presented. The plant models identified here were used to develop MISO LQG controllers for a Crushing Plant in a Sugar Mill.