On Optimization of Sensor Selection for Aircraft Gas Turbine Engines (original) (raw)
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Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics
Volume 2: Controls, Diagnostics and Instrumentation; Cycle Innovations; Electric Power, 2008
The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performan...
Adaptive Estimation Algorithm for Aircraft Engine Performance Monitoring
Journal of Propulsion and Power, 2008
In the frame of turbine engine performance monitoring, system identification procedures are often used to adapt a simulation model of the engine to some observed data through a set of so-called health parameters. Doing so, the values of these health parameters are intended to represent the actual health condition of the engine. The Kalman filter has been widely used to achieve the identification procedure in real-time onboard applications. However, to achieve a proper filtering of the measurement noise, the health parameters are often assumed to vary in time relatively slowly, preventing any abrupt accidental events from being tracked effectively. This contribution presents a procedure called adaptive filtering. Based on a covariance-matching method, it is intended to automatically release the health parameters once an accidental event is detected. This enables the Kalman filter to deal with both continuous and abrupt fault conditions. Nomenclature k = discrete time index M = size of the buffer N = rotational speed N m; R = Gaussian probability density function with mean m and covariance matrix R p 0 i = total pressure at station i T 0 i = total temperature at station i u k = control parameters w k = health parameters y k = observed measurements k = measurement noise vector ! k = process noise vector = estimated value :
Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering
Journal of Engineering for Gas Turbines and Power, 2005
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state-variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state-variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that ...
Kalman Filter Constraint Switching for Turbofan Engine Health Estimation
European Journal of Control, 2006
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state variable constraints has been shown to generally improve the filter's estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy. After all, the Kalman filter is theoretically optimal, so the incorporation of heuristic
Proxy Parameters in Gas Turbine Engine Health Monitoring
The effectiveness of decisions derived using condition monitoring techniques depends on the accuracy and relevance of the data gathered. Managers responsible for cost intensive safety critical systems face the dilemma of committing either α-error or β-error in decisions related to safe shutdown of the system to preempt a possible catastrophic accident. Noisy or cross-coupled data could mask potential hazards and thus defeat the entire purpose of condition monitoring. Increase in complexity and stringent airworthiness norms for aero gas turbine engines demand a deeper insight into the parameters of measurement and data processing to make the information gathered through condition monitoring relevant and useful for decision making. In this paper existing methodologies of aeroengine condition monitoring are reviewed and the use of proxy parameters to improve the utility of conventional health monitoring techniques proposed. The health monitoring parameters of operational aeroengines are either electronically recorded online through sophisticated instrumentation or manually captured and recorded in the relevant inspection sheets offline. For example, vibration, tip clearance and dry/wet temperatures and pressures are monitored online with high sampling rate while run down time, filter pop-up and other visual indications are recorded manually at the end of a sortie. Each of these data corresponds to a failure mode or a set of possible failure modes. Therefore, the sensitivity of the recorded parameter to the intended failure mode, adequacy of sampling frequency, knowledge of the threshold levels of parameters and cross coupling of the parameters with other environmental factors influence the quality of data gathered and hence its utility for decision making. An approach based on proxy parameters is proposed and illustrated. The proxy parameters are functions of observed variables gathered through the condition monitoring setup and are to be evaluated utilizing the computational capability available in the engine health monitoring system (EHMS). The diagnostic and prognostic capabilities of condition monitoring techniques can be enhanced by using proxy parameters. In the overall sense, this paper conceptualizes the health monitoring scenario from the decision making utility point of view and proposes proxy parameters with a relevant case study illustration.
Gas turbine component fault detection from a limited number of measurements
Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 2004
A method for detecting faults in the components of gas turbines, based on the use of non-linear engine models and optimization techniques, is presented. The method determines deviations in mass flow capacity and efficiency of individual engine components through minimization of appropriate cost function, formulated such that measurements are matched in an optimum way. Component performance deviations are expressed through appropriate modification factors, which are used as health parameters. The modification factors are coupled to a non-linear engine performance model and can represent different health conditions of the engine. The problem of fault diagnosis is formulated as the problem of determining the values of these factors from a given set of measurement data. The novel aspect of the method presented in this paper is that it can be used to determine health factors that are less, equal or larger in number than the available performance measurements. When measurements are fewer than the parameters to be determined, solutions are derived using an approach of the maximum likelihood type. It is demonstrated than such a solution can provide successful diagnosis for the majority of fault types expected to occur in an engine. The method presented is substantiated by application to a large bypass ratio, partially mixed, turbofan, typical of the large civil aircraft engine configuration in today's aircrafts. An extensive set of component faults is studied, representing malfunctions expected to occur in practice. The method is shown to perform successfully in fault identification over this set, using a limited number of measurements representative of current onboard instrumentation.
2021
This paper outlines a novel sensor selection and observer design algorithm for linear time-invariant systems with both process and measurement noise based on H 2 optimization to optimize the tradeoff between the observer error and the number of required sensors. The optimization problem is relaxed to a sequence of convex optimization problems that minimize the cost function consisting of the H 2 norm of the observer error and the weighted l 1 norm of the observer gain. An LMI formulation allows for efficient solution via semi-definite programing. The approach is applied here, for the first time, to a turbo-charged spark-ignited engine using exhaust gas circulation to determine the optimal sensor sets for real-time intake manifold burnt gas mass fraction estimation. Simulation with the candidate estimator embedded in a high fidelity engine GT-Power model demonstrates that the optimal sensor sets selected using this algorithm have the best H 2 estimation performance. Sensor redundancy...