An expected-mode augmentation-based approach for multiple-fault detection and diagnosis in flight control systems

Liu, Z-J; Li, Q; Xh, Liu; Lan, Jian; Mu, C-D

doi:10.1177/0954410011421994

Cited by 3 publications

(6 citation statements)

References 12 publications

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“…The box on the right represents the MM fault diagnosis in FCS. An expected-mode augmented variable-structure MM method is discussed in detail in our previous paper (9) ; this method can detect multiple faults with an adaptive model set. The MM method employs a series of parallel filters, each based on a model representing a particular fault mode.…”

Section: Hybrid Fault Diagnosis Scheme 21 Framework Of the Diagnosismentioning

confidence: 99%

“…where 9 are basic events equal to 0 or 1. A cut set of FT is a failure mode of the system, and the basic events of this mode are the root…”

Section: (15)mentioning

confidence: 99%

“…In FCS, the multiple-model (MM) (7,8) estimator is widely used as the fault identification method. The MM method has been discussed and improved in our previous paper (9) . It employs a bank of Kalman filters, each based on a failure model.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

Liu

Liu²

2014

Aeronaut. j. (1968)

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A cockpit instrumentation system provides various elements of information for pilots. However, logical inference based on a cockpit instruments fault tree (FT) and reliability sometimes cannot give a correct diagnosis of failures. In addition, in flight control systems (FCS), a fault identification method based on the multiple-model (MM) estimator cannot find the basic fault cause. To deal with these problems, a hybrid approach which is capable of integrating inference and fault identification is proposed. In this approach, the event nodes of the FT which have correlations to the FCS are separated into modules. Each module corresponds to a fault mode of the FCS. To use these correlations, fault inference and the MM estimator can share fault diagnosis information. Simulation results show that the proposed approach is helpful in detecting the root cause of failure and is more correct than single fault diagnosis method.

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Section: Hybrid Fault Diagnosis Scheme 21 Framework Of the Diagnosismentioning

confidence: 99%

“…where 9 are basic events equal to 0 or 1. A cut set of FT is a failure mode of the system, and the basic events of this mode are the root…”

Section: (15)mentioning

confidence: 99%

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A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

Liu

Liu²

2014

Aeronaut. j. (1968)

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“…For example, Steven et al applied statistical analysis tools to obtain a nonlinear power system based on the power system's voltage detection. [7][8][9][10][11][12] In aerospace aviation, the spacecraft flywheel failure was also achieved by using a limited fault reconstruction method. Zhang et al combined spacecraft telemetry data with data mining and the classical a priori in-depth searching algorithm that is very useful for processing a smooth noise, filling the vacancy value, and removing the inconsistencies.…”

Section: Introductionmentioning

confidence: 99%

“…applied statistical analysis tools to obtain a nonlinear power system based on the power system’s voltage detection. 7–12 In aerospace aviation, the spacecraft flywheel failure was also achieved by using a limited fault reconstruction method. Zhang et al.…”

Section: Introductionmentioning

confidence: 99%

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

Song

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics' data, improve the accuracy of identification, and shorten the computing time with high efficiency.

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Joint target tracking and identification in complex traffic scene

Cao

et al. 2022

EURASIP J. Adv. Signal Process.

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In practical road traffic scene, targets usually face high ground clutter, high and variable motion, high nonlinearity, which lead to targets tracking or identification challenging. What’s more, tracking and identification are usually interdependent in reality, and thus it is promising to solve them jointly. In this paper, we propose a novel joint tracking and identification (JTI) scheme to handle such problems involving coupled tracking and identification, i.e., JTI problems. Specifically, we formulate the JTI problem in complex traffic scene using a hybrid system. Then, by exploiting the generalized Bayes risk for JTI, we derive analytical estimator and decider for the coupling of tracking and identification in complex road targets motion. Furthermore, an unscented Kalman filter-expected mode augmentation-based estimation strategy is creatively developed to improve both estimation and decision performance. In additions, a joint performance evaluation metric is presented to assess the performance the joint of the proposed JTI scheme. Finally, two simulation examples under different traffic scenarios demonstrate that the proposed JTI approach outperforms the traditional tracking-then-identification and identification-then-tracking methods in joint performance.

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An expected-mode augmentation-based approach for multiple-fault detection and diagnosis in flight control systems

Cited by 3 publications

References 12 publications

A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

Joint target tracking and identification in complex traffic scene

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