Towards self-tuning residual generators for UAV control surface fault diagnosis

Efficient extraction of fault signatures from sensory data is a major concern in fault diagnosis. This paper introduces a self-tuning method of fault signature extraction that enhances fault detection, minimizes false alarms, improves diagnosability, and reduces fault signature variability. The proposed method uses a Flagging Unit to convert the processed measurements to binary vectors, and utilizes nonparametric pattern classification techniques to estimate the fault signatures. The performance of the Flagging Unit, which relies on its adaptation algorithms to optimize its performance based upon a sample batch of measurement-fault vectors, is demonstrated in simulation.

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A fuzzy system for automotive fault diagnosis: fast rule generation and self-tuning

IEEE Transactions on Vehicular Technology ◽

10.1109/25.832997 ◽

2000 ◽

Vol 49 (2) ◽

pp. 651-660 ◽

Cited By ~ 18

Author(s):

Yi Lu ◽

Tie Qi Chen ◽

B. Hamilton

Keyword(s):

Fault Diagnosis ◽

Fuzzy System ◽

Rule Generation ◽

Self Tuning

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Application of H-infinity fault diagnosis to ADDSAFE benchmark: the control surface jamming case

AIAA Guidance, Navigation, and Control Conference ◽

10.2514/6.2011-6677 ◽

2011 ◽

Cited By ~ 5

Author(s):

Andres Marcos

Keyword(s):

Fault Diagnosis ◽

Control Surface ◽

H Infinity

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Control surface fault diagnosis with specified detection probability — Real event experiences

2013 International Conference on Unmanned Aircraft Systems (ICUAS) ◽

10.1109/icuas.2013.6564729 ◽

2013 ◽

Cited By ~ 5

Author(s):

Soren Hansen ◽

Mogens Blanke

Keyword(s):

Fault Diagnosis ◽

Detection Probability ◽

Control Surface

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Sensor/Control Surface Fault Diagnosis and Reconfiguration in Aircraft Control Systems

Engineering, Construction, and Operations in Challenging Environments ◽

10.1061/40722(153)25 ◽

2004 ◽

Author(s):

Ch. Hajiyev ◽

F. Caliskan

Keyword(s):

Fault Diagnosis ◽

Control Systems ◽

Control Surface ◽

Aircraft Control ◽

Sensor Control

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Self-tuning fault diagnosis of MEMS

Mechatronics ◽

10.1016/j.mechatronics.2013.08.006 ◽

2013 ◽

Vol 23 (8) ◽

pp. 1094-1099 ◽

Cited By ~ 13

Author(s):

Afshin Izadian

Keyword(s):

Fault Diagnosis ◽

Self Tuning

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Characterization of carbides in M50NiL

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100087343 ◽

1991 ◽

Vol 49 ◽

pp. 606-607

Author(s):

L. S. Lin ◽

K. P. Gumz ◽

A. V. Karg ◽

C. C. Law

Keyword(s):

Temperature Effects ◽

Base Composition ◽

Lattice Parameter ◽

Control Surface ◽

Carbide Formation ◽

Particle Sizes ◽

Low Carbon ◽

Fee Structure ◽

Heat Treated

Carbon and temperature effects on carbide formation in the carburized zone of M50NiL are of great importance because they can be used to control surface properties of bearings. A series of homogeneous alloys (with M50NiL as base composition) containing various levels of carbon in the range of 0.15% to 1.5% (in wt.%) and heat treated at temperatures between 650°C to 1100°C were selected for characterizations. Eleven samples were chosen for carbide characterization and chemical analysis and their identifications are listed in Table 1.Five different carbides consisting of M6C, M2C, M7C3 and M23C6 were found in all eleven samples examined as shown in Table 1. M6C carbides (with least carbon) were found to be the major carbide in low carbon alloys (<0.3% C) and their amounts decreased as the carbon content increased. In sample C (0.3% C), most particles (95%) encountered were M6C carbide with a particle sizes range between 0.05 to 0.25 um. The M6C carbide are enriched in both Mo and Fe and have a fee structure with lattice parameter a=1.105 nm (Figure 1).

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