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Analytical redundant sensor failure detection, isolation and accommodation techniques for gas turbine engines are surveyed. Both the theoretical technology base and demonstrated concepts are discussed. Also included is a discussion of current technology needs and ongoing Government sponsored programs to meet those needs.
Analytical redundant sensor failure detection, isolation and accommodation techniques for gas turbine engines are surveyed. Both the theoretical technonlogy base and demonstrated concepts are discussed. Also included is a discussion of current technology needs and angoing Government sponsored programs to meet those needs.
The concept of analytic redundancy is extended to identify nonzero constant output failures in large spacecraft active control sensors and actuators, using a set of distributed sensors which do not nominally produce equivalent signals but are related through the structural dynamics of the system. The suboptimal technique uses a Sequential Probability Ratio Test on the residual sequence of a Kalman filter based on a modal structure model, to determine failure of a component based on an assumed failure hypothesis. The specific failure is identified and then the Kalman filter gains are reconfigured for the identified remaining working sensor set. Experimental data using an apparatus whose dynamics are representative of a large spacecraft show the nominal filter performance under failed and unfailed conditions.
This paper deals with analytic redundancy management of systems that have appreciable structural dynamics and require active control. The class of systems considered is large, lightweight spacecraft that have large numbers of distributed sensors and actuators. Both preliminary design and on-line operations are studied. For the preliminary design we deal with the placement of the sensor and actuator components on a highly flexible spacecraft. For on-line operation an analytic redundancy management system based on examination of the residuals of a Kalman filter is considered. A large, flexible grid made of overlapping aluminum bars is used to experimentally evaluate this analytic redundancy management system. Results of the experimental evaluation are included in the paper.
This paper reviews the formulation and flight test results of an algorithm to detect and isolate the first failure of any one of twelve duplex control sensor signals being monitored. The technique uses like-signal differences for fault detection while relying upon analytic redundancy relationships among unlike quantities to isolate the faulty sensor. The fault isolation logic utilizes the modified sequential probability ratio test, which explicitly accommodates the inevitable irreducible low frequency errors present in the analytic redundancy residuals. In addition, the algorithm uses sensor output selftest, which takes advantage of the duplex sensor structure by immediately removing a highly erratic sensor from control calculations and analytic redundancy relationships while awaiting a definitive fault isolation decision via analytic redundancy. This study represents a proof of concept demonstration of a methodology that can be applied to duplex or higher flight control sensor configurations and, in addition, can monitor the health of one simplex signal per analytic redundancy relationship.
The formulation and flight test results of an algorithm to detect and isolate the first failure of any one of 12 duplex control sensor signals (24 in all) being monitored are reviewed. The technique uses like signal differences for fault detection while relying upon analytic redundancy relationships among unlike quantities to isolate the faulty sensor. The fault isolation logic utilizes the modified sequential probability ratio test, which explicitly accommodates the inevitable, irreducible low frequency errors present in the analytic redundancy residuals. In addition, the algorithm used sensor output selftest, which takes advantage of the duplex sensor structure by immediately removing a hard failed sensor from control calculations and analytic redundancy relationships while awaiting a definitive fault isolation decision via analytic redundancy. This study represents a proof of concept demonstration of a methodology that is applied to duplex or higher flight control sensor configurations and, in addition, can monitor the health of one simplex signal per analytic redundancy relationship.
The formulation and flight test results of an algorithm to detect and isolate the first failure of any one of twelve duplex control sensors being monitored are described. The technique uses like sensor output differences for fault detection while relying upon analytic redundancy relationships among unlike quantities to isolate the faulty sensor. The fault isolation logic utilizes the modified sequential probability ratio test, which explicitly accommodates the inevitable irreducible low frequency errors present in the analytic redundancy residuals. In addition, the algorithm uses sensor output selftest, which takes advantage of the duplex sensor structure by immediately removing a highly erratic sensor from control calculations and analytic redundancy relationships while awaiting a definitive fault isolation decision via analytic redundancy.
The use of sensor redundancy is crucial in aerospace systems to maintain safe, reliable operation. While hardware redundancy is more common in application, analytical redundancy can provide a viable alternative in systems where the installation of multiple redundant sensors is not viable. To this end, the use of Kalman filters to analytically validate sensor measurements within rocket engines was explored. First, a dynamic model of the RS 25 engine, a derivative of the Space Shuttle Main Engine (SSME), was reduced to a subset of relations, focused around the main combustion chamber pressure. These relations were used within the Kalman filter algorithm to generate an estimate of sensor measurements to be compared with true measurements for data validation purposes. By using a bank of Kalman filters, the residuals between the estimated and true measurements were used to detect and isolate sensor faults. Through fault simulations, the sensor validation performance of this Kalman filter bank design was compared to a hardware redundancy check. Sensor bias and drift faults of various magnitudes were injected into nominal RS 25 engine test data. Results for both approaches show comparable fault detection with most bias faults found nearly instantaneously by both algorithms. Drift fault detection results show certain cases where one algorithm is faster than the other. The key advantage of the Kalman filter algorithm is shown in fault isolation performance where it can isolate faults between two redundant sensors while the hardware redundancy comparisons cannot.
An analytic redundancy management approach for on-line component failure detection is described and illustrated using a simulation of the NASA Langley Spacecaft Control Laboratory Experiment (SCOLE) research facility. The SCOLE experimental apparatus simulated is a functional model of the Space Shuttle with a large, flexible, offset-feed antenna cantilevered from the payload bay. This approach uses a single, active, Kalman filter selected from a bank of filters, each element of which was previously designed to accommodate a specific failure condition. The residuals of this active filter are processed through a sequential probability ratio test (SPRT) filter to identify the failure state of the system and, hence, closing the failure accommodation loop, to select the active filter. Results are presented that illustrate the ability of the system to detect and recover from failures introduced in the angular rate and linear acceleration sensors on the SCOLE facility.
The study involves the bank of filters approach to analytical redundancy management since this is amenable to microelectronic implementation. Attention is given to a study of the UD factorized filter to determine if it gives more accurate estimates than the standard Kalman filter when data processing word size is reduced. It is reported that, as the word size is reduced, the effect of modeling error dominates the filter performance of the two filters. However, the UD filter is shown to maintain a slight advantage in tracking performance. It is concluded that because of the UD filter's stability in the serial processing mode, it remains the leading candidate for microelectronic implementation.
The objective of this work is to develop a practical sensor analytic redundancy management scheme for flexible spacecraft and to demonstrate it using the SCOLE experimental apparatus. The particular scheme to be used is taken from previous work on the Grid apparatus by Williams and Montgomery.
This research involves the development of a knowledge-based fault-tolerant flight control system. A software architecture is presented that integrates quantitative analytical redundancy techniques and heuristic expert system problem solving concepts for the purpose of in-flight, real-time failure accommodation.
The structure of a sensor failure detection and identification system designed for the NASA F-8 DFBW aircraft is outlined. The system is for use in a dual-redundant environment, and it takes maximal advantage of all functional relationships among the sensed variables. The identification logic uses the quality sequential probability ratio, which provides a useful on-line measure of confidence in the various forms of analytic redundancy. Preliminary simulation results indicate good behavior of the analytic decision statistic, based on the sequential probability ratio test.
The Failure Detection and Identification (FDI) process is viewed as consisting of two stages: residual generation and decision making. It is argued that a robust FDI system can be achieved by designing a robust residual generation process. Analytical redundancy, the basis for residual generation, is characterized in terms of a parity space. Using the concept of parity relations, residuals can be generated in a number of ways and the design of a robust residual generation process can be formulated as a minimax optimization problem. An example is included to illustrate this design methodology. Previously announcedd in STAR as N83-20653
In this paper we present a reliable technique for failure detection and identification (FDI) for dual redundant flight control sensors aboard the NASA F-8 digital fly-by-wire (DFBW) aircraft, and we discuss the successful application of the technique to identifying failures injected on test flight telemetry data. The technique exploits the analytic redundancy which exists as relationships among variables being measured by dissimilar instruments. With straightforward modification the technique may be extended to provide failure monitoring of a single remaining sensor after the identified failure of its companion sensor.
This paper presents a design method for optimal redundancy management for nonlinear systems with application to highly maneuvering aircraft. The approach taken is based on selecting the failure states to be covered by the system design and constructing a cost function that represents the cost of making an incorrect decision. The decision logic which minimizes the cost requires a bank of extended Kalman filters running in parallel. This produces a severe computational requirement. To reduce this requirement, a suboptimal logic is developed based on using a nonlinear single-stage prediction algorithm in the filters with filter gains and decision logic selected using steady-state results obtained from a linearization of the vehicle and sensor dynamics. The design process is then applied to designing a redundancy management system for the F8-C aircraft. Results indicate that the system is superior in failure detection to a system using the same structure but using a linear single-stage prediction algorithm in the filters.
The details are presented of an onboard digital computer algorithm designed to reliably detect and isolate the first failure in a duplex set of flight control sensors aboard the NASA F-8 digital fly-by-wire aircraft. The algorithm's successful flight test program is summarized, and specific examples are presented of algorithm behavior in response to software-induced signal faults, both with and without aircraft parameter modeling errors.
A hypothetical turbofan engine simplified simulation with a multivariable control and sensor failure detection, isolation, and accommodation logic (HYTESS II) is presented. The digital program, written in FORTRAN, is self-contained, efficient, realistic and easily used. Simulated engine dynamics were developed from linearized operating point models. However, essential nonlinear effects are retained. The simulation is representative of the hypothetical, low bypass ratio turbofan engine with an advanced control and failure detection logic. Included is a description of the engine dynamics, the control algorithm, and the sensor failure detection logic. Details of the simulation including block diagrams, variable descriptions, common block definitions, subroutine descriptions, and input requirements are given. Example simulation results are also presented.