Diagnosis of Incipient Sensor Faults in a Flight Control Actuation System

Jayakumar, M; Das, B.B.

doi:10.1109/sice.2006.315126

Cited by 4 publications

(3 citation statements)

References 14 publications

(9 reference statements)

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“…Kalman filtering [14] is a state and parameter estimation technique that fuses data from different sensors together to produce an accurate estimate of the true system state. Jayakumar and Das [37] use a single Kalman filter, driven by the motor shaft velocity sensor, to diagnose problems in a flight control system. They diagnose incipient sensor faults using structured residuals that are generated using the Kalman filter estimates.…”

Section: Aerospacementioning

confidence: 99%

Failure Diagnosis of Complex Systems

Kavulya

Joshi

Giandomenico

et al. 2012

Resilience Assessment and Evaluation of Computing Systems

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Failure diagnosis is the process of identifying the causes of impairment in a system's function based on observable symptoms, i.e., determining which fault led to an observed failure. Since multiple faults can often lead to very similar symptoms, failure diagnosis is often the first line of defense when things go wrong -a prerequisite before any corrective actions can be undertaken. The results of diagnosis also provide data about a system's operational fault profile for use in offline resilience evaluation. While diagnosis has historically been a largely manual process requiring significant human input, techniques to automate as much of the process as possible have significantly grown in importance in many industries including telecommunications, internet services, automotive systems, and aerospace. This chapter presents a survey of automated failure diagnosis techniques including both model-based and model-free approaches. Industrial applications of these techniques in the above domains are presented, and finally, future trends and open challenges in the field are discussed.

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Section: Aerospacementioning

confidence: 99%

Failure Diagnosis of Complex Systems

Kavulya

Joshi

Giandomenico

et al. 2012

Resilience Assessment and Evaluation of Computing Systems

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show abstract

“…In reference [1], the Kalman filter was designed to the diagnosis of incipient sensor faults. However, the unknown inputs such as system disturbances and noises were not considered in this paper.…”

Section: Introductionmentioning

confidence: 99%

Sensors Fault Detection and Diagnosis Based On Morphology-wavelet Algorithm

Hou

Zhang

2008

2008 IEEE Conference on Robotics, Automation and Mechatronics

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This paper proposed a novel method to fault detection and diagnosis of sensors using trend analysis of input and output signals related to the sensor itself. Firstly, generalized morphological filter with multi-structure elements is designed to filter the random noise and impulse noise in sensor's input and output signals. And secondly, to effectively extract the incipient fault and abruptly fault characteristic, a wavelet transform was used to decompose and analyze the filtered signals in this paper. Through the Multi Resolution Analysis (MRA), the fault can be located accurately. There typical sensor faults such as fix, gain, bias, drift faults were studied. The simulation results show that this algorithm is capable of locating accurately.

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“…Design and integration of a flight control systems often includes fault detection algorithms in order to make the system more robust against failures. There are many studies on fault detection and diagnosis of different faulty cases in flight control systems such as; oscillatory failure case [1], stall load [2], jamming [3], runaway [4], and incipient sensor failures [5]. One of the most important of them all is jamming.…”

mentioning

confidence: 99%

A Model - Based Jamming Detection Method for Electro-Hydrostatic Actuators

Poyraz

Turgut

Caliskan

2020

Hittite J. Sci. Eng.

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A n anomaly or a fault, which may be treated as insignificant in a complicated engineering system, like an electro-hydraulic flight control actuator, can cause considerable performance degradation and deteriorating effects or even worse it may end up with a catastrophic system failure. One of the problems commonly observed in air vehicles is called jamming. Jamming is the type of failure where an actuator connected to a flight control surface permanently gets stuck at a certain position and does not move anymore. This might cause a loss of control of the air vehicle causing severe consequences. Although, there are several methods to detect jamming in the literature, still there is not a universally accepted solution. In this paper, a novel threshold-based fault detection and diagnosis method is proposed. The method is based on the variance of time rate of change of disturbance load and the average tracking error. When these two quantities are above a certain corresponding threshold, jamming is detected. The method is tested on an experimental setup. The experiments show that the detection performance of the method is satisfactory and the detection is performed under one second for all the test cases.

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Diagnosis of Incipient Sensor Faults in a Flight Control Actuation System

Cited by 4 publications

References 14 publications

Failure Diagnosis of Complex Systems

Failure Diagnosis of Complex Systems

Sensors Fault Detection and Diagnosis Based On Morphology-wavelet Algorithm

A Model - Based Jamming Detection Method for Electro-Hydrostatic Actuators

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