Multiple model adaptive estimation with filter spawning

Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the VISTA F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream." Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuaton-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared with what is possible without spawning additional filters, making partial failure adaptation a viable methodology.     

Sensor/actuator failure detection in the Vista F-16 by multiplemodel adaptive estimation

Multiple model adaptive estimation (MMAE) is applied to the Variable-In-flight Stability Test Aircraft (VISTA) F-16 flight control system at a low dynamic pressure flight condition (0.4 M at 20000 ft). Single actuator and sensor failures are addressed first, followed by dual actuator and sensor failures. The system is evaluated for complete or “hard” failures, partial or, “soft” failures, and combinations of hard and soft actuator and sensor failures. Residual monitoring is discussed for single and dual failure scenarios. Performance is enhanced by the application of a modified Bayesian form of MMAE, scalar residual monitoring to reduce ambiguities, automatic dithering where advantageous, and purposeful commands     

Data mining technology for failure prognostic of avionics

Adverse environmental conditions have combined cumulative effects leading to performance degradation and failures of avionics. Classical reliability addresses statistically-generic devices and is less suitable for the situations when failures are not traced to manufacturing but rather to unique operational conditions of particular hardware units. An approach aimed at the accurate assessment of the probability of failure of any avionics unit utilizing the known history-of-abuse from environmental and operational factors is presented herein. The suggested prognostic model utilizes information downloaded from dedicated monitoring systems of flight-critical hardware and stored in a database. Such a database can be established from the laboratory testing of hardware and supplemented with real operational data. This approach results in a novel knowledge discovery from data technology that can be efficiently used in a wide area of applications and provide a quantitative basis for the modern maintenance concept known as service-when-needed. An illustrative numerical example is provided     

Characterization of Kalman filter residuals in the presence ofmismodeling

The mean and covariance of a Kalman filter residual are computed for specific cases in which the Kalman filter model differs from a linear model that accurately represents the true system (the truth model). Multiple model adaptive estimation (MMAE) uses a bank of Kalman filters, each with a different internal model, and a hypothesis testing algorithm that uses the residuals from this bank of Kalman filters to estimate the true system model. At most, only one Kalman filter model will exactly match the truth model and will produce a residual whose mean and standard deviation have already been analyzed. All of the other filters use internal models that mismodel the true system. We compute the effects of a mismodeled input matrix, output matrix, and state transition matrix on these residuals. The computed mean and covariance are compared with simulation results of flight control failures that correspond to mismodeled input matrices and output matrices     

A new algorithm to optimize Barker code sidelobe suppressionfilters

The authors suggest a new algorithm for binary coding waveform sidelobe reduction after matched filtering and present a general method by which optimized sidelobe suppression filters for Barker codes can be obtained with a peak output sidelobe 2.62 dB lower than the results found in the literature (for 13-b Barker code). This optimization algorithm is also promising for other binary coding waveforms, such as truncated pseudonoise (PN) sequences and concatenated codes. This new approach can readily be applied to sidelobe-reduction filter design for other binary coding waveforms, such as truncated PN sequences, concatenated codes, etc., which often find their applications in radar systems and spread spectrum communication systems     

竞争失效场合步进应力加速试验统计分析

竞争失效场合加速试验(AT)技术是加速试验由简单结构产品向复杂结构产品推广应用的基础.但目前的方法主要针对恒定应力加速试验.而对竞争失效场合步进应力加速试验缺乏相关研究.针对这一问题,对最一般形式(突发型失效和退化型失效并存)的竞争失效场合步进应力试验进行建模与分析.充分考虑了试验数据由于试验截尾和失效样本对应的失效模...     

Enhanced motion and sizing of bank in moving-bank MMAE

The focus of this research is to provide methods for generating precise parameter estimates in the face of potentially significant parameter variations such as system component failures. The standard multiple model adaptive estimation (MMAE) algorithm uses a bank of Kalman filters, each based on a different model of the system. Parameter discretization within the MMAE refers to selection of the parameter values assumed by the elemental Kalman filters, and dynamically redeclaring such discretization yields a moving-bank MMAE. A new online parameter discretization method is developed based on the probabilities associated with the generalized chi-squared random variables formed by residual information from the elemental Kalman filters within the MMAE. This new algorithm is validated through computer simulation of an aircraft navigation system subjected to interference/jamming while attempting a successful precision landing of the aircraft.     

采用一组卡尔曼滤波器检测发动机传感器故障

在发动机全功能数字电子控制系统中,提高传感器工作的可靠性是十分重要的,除了不断对传感器本身的性能加以改进提高外,现在广泛地采用了余度技术。近二十年来对解析余度(Analyt ical Redundancy)进行了广泛的研究,解析余度(AR)方法是基于各状态变量之间存在的解析关系,在系统可观条件下,利用无故障的输出测量值去估计(构造)已故障传感器正常工作状态时的输出信息,从而实现对故障的检测、隔离与重构,保证控制系统具有预定的控制性能。      

用于检测操纵面损伤的故障检测滤波器

针对飞行中典型的操纵面损伤故障,以故障检测滤波器为基础,开发了一种建立在离散模型上的快速、稳定的故障检测及诊断算法,可以在几个采样周期内同时精确检测出多个操纵面的损伤程度,故障检测滤波器的稳定性、收敛性也得到证明,具有较好的实用性。几种组合故障下的仿真研究证实了上述结论。     

隐蔽失效适航要求符合性验证分析

隐蔽失效的适航符合性验证对飞机系统安全性水平会产生重大影响。经过十余年论证,EASA在CS/AMC 25.1309中新增了隐蔽失效的要求。例如要求尽可能消除重大隐蔽失效;为了防止再发生一个失效即导致灾难性失效状态的情况,对由两个失效导致的每个灾难性失效状态,其中任一个在大于单次飞行中是隐蔽失效,要求从隐蔽限制方面限制运行时间,并且从剩余概率限制方面假定一个隐蔽失效已发生时限制所有单个显性失效组合的平均概率。针对系统安全性条款CS 25.1309(b)新增的隐蔽失效相关要求,分析条款的符合性方法,通过典型故障树分析及其最小割集的示例,说明隐蔽限制和剩余概率限制准则的应用方法。从工程角度说明了条款的应用范围,给出了隐蔽失效的概率和限制暴露时间的计算方法,提升型号系统安全性水平。     

Detection of interference/jamming and spoofing in a DGPS-aidedinertial system

Previous research at the Air Force Institute of Technology (AFIT) has resulted in the design of a differential Global Positioning System (DGPS) aided INS-based (inertial navigation system) precision landing system (PLS) capable of meeting the FAA precision requirements for instrument landings. The susceptibility of DGPS transmissions to both intentional and nonintentional interference/jamming and spoofing must be addressed before DGPS may be safely used as a major component of such a critical navigational device. This research applies multiple model adaptive estimation (MMAE) techniques to the problem of detecting and identifying interference/jamming and spoofing in the DGPS signal. Such an MMAE is composed of a bank of parallel filters, each hypothesizing a different failure status, along with an evaluation of the current probability of each hypothesis being correct, to form a probability-weighted average state estimate as an output. For interference/jamming degradation represented as increased measurement noise variance, simulation results show that, because of the good failure detection and isolation (FDI) performance using MMAE, the blended navigation performance is essentially that of a single extended Kalman filter (EKF) artificially informed of the actual interference noise variance. However, a standard MMAE is completely unable to detect spoofing failures (modeled as a bias or ramp offset signal directly added to the measurement). This work describes a moving-bank pseudoresidual MMAE (PRMMAE) to detect and identify such spoofing. Using the PRMMAE algorithm, spoofing is very effectively detected and isolated; the resulting navigation performance is equivalent to that of an EKF operating in an environment without spoofing     

Terrain database integrity monitoring for synthetic vision systems

A real-time terrain database integrity monitor for synthetic vision systems (SVS) that are to be used in civil aviation is presented. SVS provides pilots with advanced display technology including terrain information as well as other information about the external environment such as obstacles and traffic. The use of SVS to support strategic and tactical decision-making and the compelling nature of the terrain depiction may require terrain database server certification at the essential and flight-critical levels. SVS and terrain database characteristics are discussed and a failure model is identified. Real-time integrity monitors are proposed that check the consistency between terrain profiles described by the database and terrain profiles that are sensed in flight by either a downward-looking (DWL) sensor or a forward-looking (FWL) season A DWL sensor scheme is discussed in detail and it is shown that this scheme can provide the necessary integrity required for an essential certification of a terrain database server.     

Dual Speed PN Ranging System for Tracking of Deep Space Probes

A new pseudonoise (PN) ranging system is proposed for trackingof deep space probes, named the dual speed PN ranging system,that transmits two different PN sequences simultaneously through a PSK/PSK (phase-shift keying) dual speed communication channel.The dual speed PN ranging system improves the maximummeasurable range significantly, with no degradation of theacquisition time, when compared with usual PN ranging systems.According to the analysis, the tracking error of this system becomesas small as those of usual PN ranging systems when the inputsignal-to-noise ratio (SNR) is large enough.     

Reconfigurable flight control via multiple model adaptive controlmethods

An aircraft flight control system with reconfigurable capabilities is considered. A multiple model adaptive controller (MMAC) is shown to provide effective reconfigurability when subjected to single and double failures of sensors and/or actuators. A command generator tracker/proportional-plus-integral/Kalman filter (CGT/PI/KF) form of controller was chosen for each of the elemental controllers within the MMAC algorithm and each was designed via LQG synthesis to provide desirable vehicle behavior for a particular failure status of sensors and actuators. The MMAC performance is enhanced by an alternate computation of the MMAC hypothesis probabilities, use of maximum a posteriori probability (MAP) versus Bayesian form of the MAC (or a modified combination of both), and reduction of identification ambiguities through scalar residual monitoring for the case of sensor failures     

竞争风险模型下变环境的发动机叶片可靠性分析

针对航空发动机转子叶片的恶劣工况导致其存在多种故障模式,各种故障的失效与使用环境紧密关联,给航空发动机转子叶片的可靠性分析或风险控制带来了难度这一问题,从转子叶片的磨损和裂纹两种主要故障模式特点出发,研究了变环境下的转子叶片磨损故障模型和疲劳裂纹故障模型,提出了一种基于竞争风险模型的转子叶片可靠性分析方法,并给出了求解算法;以某高压涡轮转子叶片为例进行了分析研究.结果表明:在可靠性分析中采用单个故障模型比竞争风险模型风险更大;且在竞争风险模型下,如果不考虑推力环境的影响,以不可靠度要求0.1为例,相应风险增加了33%,验证了所提方法的实用性.      

Enhancements to a multiple model adaptive estimator/image-tracker

Three major enhancements to a previously devised multiple model adaptive estimator (MMAE) for target image tracking are developed and analyzed. These are: allowing some of the elemental filters to have rectangular fields of view and to be tuned for target dynamics that are harsher in one direction than others; considering both Gauss-Markov acceleration models and constant turn-rate models for target dynamics; and devising an initial target acquisition algorithm to remove important biases in the estimated target template to be used in a correlator within the tracker. Particularly good adaptation responsiveness is demonstrated in the multiple model algorithm's ability to handle harsh maneuver onset, yielding performance essentially equivalent to that of the best artificially informed tracking algorithm     

A study of flight-critical computer system recovery from spaceradiation-induced error

It is well known that space radiation, containing energetic particles such as protons and ions, can cause anomalies in digital avionics onboard satellites, spacecraft, and aerial vehicles flying at high altitude. Semiconductor devices embedded in these applications become more sensitive to space radiation as the features shrink in size. One of the adverse effects of space radiation on avionics is a transient error known as single event upset (SEU). Given that it is caused by bit-flips in computer memory, SEU does not result in a damaged device. However, the SEU induced data error propagates through the run-time operational flight program, causing erroneous outputs from a flight-critical computer system. This study was motivated by a need for a cost-effective solution to keep flight-critical computers functioning after SEU occurs. The result of the study presents an approach to recover flight-critical computer systems from SEU induced error by using an identity observer array. The identity observers replicate the state data of the controller in distinct data partitions. The faulty controller can be recovered by replacing data image of the faulty data partition with that of the healthy data partition. The methodology of applying such an approach from the fault tolerant control perspective is presented. The approach is currently being tested via computer simulation     

Multiple-model adaptive estimation using a residual correlationKalman filter bank

We propose a modified multiple model adaptive estimation (MMAE) algorithm that uses the time correlation of the Kalman filter residuals, in place of their scaled magnitude, to assign conditional probabilities for each of the modeled hypotheses. This modified algorithm, denoted the residual correlation Kalman filter bank (RCKFB), uses the magnitude of an estimate of the correlation of the residual with a slightly modified version of the usual MMAE hypothesis testing algorithm to assign the conditional probabilities to the various hypotheses that are modeled in the Kalman filter bank within the MMAE. This concept is used to detect flight control actuator failures, where the existence of a single frequency sinusoid (which is highly time correlated) in the residual of an elemental filter within an MMAE is indicative of that filter having the wrong actuator failure status hypothesis. This technique results in a delay in detecting the flight control actuator failure because several samples of the residual must be collected before the residual correlation can be estimated. However, it allows a significant reduction of the amplitude of the required system inputs for exciting the various system modes to enhance identifiability, to the point where they may possibly be subliminal, so as not to be objectionable to the pilot and passengers     

Detection and diagnosis of sensor and actuator failures using IMMestimator

An approach to detection and diagnosis of multiple failures in a dynamic system is proposed. It is based on the interacting multiple-model (IMM) estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural as well as parametric changes. The proposed approach provides an integrated framework for fault detection, diagnosis, and state estimation. It is able to detect and isolate multiple faults substantially more quickly and more reliably than many existing approaches. Its superiority is illustrated in two aircraft examples for single and double faults of both sensors and actuators, in the forms of “total”, “partial”, and simultaneous failures. Both deterministic and random fault scenarios are designed and used for testing and comparing the performance fairly. Some new performance indices are presented. The robustness of the proposed approach to the design of model transition probabilities, fault modeling errors, and the uncertainties of noise statistics are also evaluated     

Performance enhancement of a multiple model adaptive estimator

We describe performance improvement techniques for a multiple model adaptive estimator (MMAE) used to detect and identify control surface and sensor failures on an unmanned flight vehicle. Initially failure identification was accomplished within 4 s of onset, but by removing the “β dominance” effects, bounding the hypothesis conditional probabilities, retuning the Kalman filters, increasing the penalty for measurement residuals, decreasing the probability smoothing, and increasing residual propagation, the identification time was reduced to 2 s