Fault diagnosis with matrix analysis for electrically actuated unmanned aerial vehicles

Kladis, Georgios P.; Economou, J.T.; Tsourdos, Antonios; White, Brian; Knowles, K.

doi:10.1243/09544100jaero422

Cited by 4 publications

(4 citation statements)

References 32 publications

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“…Despite of their success, DPCA and DPLS have been reported not to be as efficient as other state-space based methodologies such as Canonical Variate Analysis (CVA) when applied to systems working under variable loading conditions, principally due to the representation of the system dynamics (Juricek, Seborg & Larimore, 2004;Russell, Chiang & Braatz, 2000). Other popular approaches typically used for condition monitoring are neural networks, machine learning or fault-tree analysis (Chiang, Rusell & Braatz, 2000;Kladis, Economou, Tsourdos, White & Knowles, 2009).…”

Section: Introductionmentioning

confidence: 99%

Statistical process monitoring of a multiphase flow facility

Ruiz-Cárcel

Cao

Lao

et al. 2015

Control Engineering Practice

217

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a b s t r a c tIndustrial needs are evolving fast towards more flexible manufacture schemes. As a consequence, it is often required to adapt the plant production to the demand, which can be volatile depending on the application. This is why it is important to develop tools that can monitor the condition of the process working under varying operational conditions. Canonical Variate Analysis (CVA) is a multivariate data driven methodology which has been demonstrated to be superior to other methods, particularly under dynamically changing operational conditions. These comparative studies normally use computer simulated data in benchmark case studies such as the Tennessee Eastman Process Plant (Ricker, N.L. Tennessee Eastman Challenge Archive, Available at 〈http://depts.washington.edu/control/LARRY/TE/down load.html〉 Accessed 21.03.2014).The aim of this work is to provide a benchmark case to demonstrate the ability of different monitoring techniques to detect and diagnose artificially seeded faults in an industrial scale multiphase flow experimental rig. The changing operational conditions, the size and complexity of the test rig make this case study an ideal candidate for a benchmark case that provides a test bed for the evaluation of novel multivariate process monitoring techniques performance using real experimental data. In this paper, the capabilities of CVA to detect and diagnose faults in a real system working under changing operating conditions are assessed and compared with other methodologies. The results obtained demonstrate that CVA can be effectively applied for the detection and diagnosis of faults in real complex systems, and reinforce the idea that the performance of CVA is superior to other algorithms.

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Section: Introductionmentioning

confidence: 99%

Statistical process monitoring of a multiphase flow facility

Ruiz-Cárcel

Cao

Lao

et al. 2015

Control Engineering Practice

217

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“…FTA is a deductive, top down approach, which is mainly used to analyze system failures. 10 Most research works are in the field of system fault diagnosis and assessment, [11][12][13][14][15][16] except for a few studies dealing with special cases involving structures. Specifically, a fault tree technique was used to examine the static tensile failure of a fibrous composite laminate from a micromechanical aspect.…”

Section: Composite Structure Fault Tree Constructionmentioning

confidence: 99%

Fault tree analysis for composite structural damages

Chen

Ren

Bil

2013

Proceedings of the Institution of Mechanical Engineers, Part G:

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Composite airframes suffer from complex damage modes during operation. Many investigations tend to look into specific aspects of damage mechanisms but seldom take a systematic view. This paper introduces a new fault tree methodology to synthesize various damage modes of composite structures by identifying possible damage causes. Qualitative analysis is performed incorporating structure importance analysis, probability importance analysis and relative probability importance analysis. Quantitative analysis by Monte Carlo simulation is then conducted as a validation to demonstrate the feasibility of the fault tree for composite damages. A number of options addressing main damage causes are proposed to improve the reliability of composite structures. Engineers from airlines and manufacturers can use this method to prioritize the main damage causes in different situations as a failure preventative tool or damage evaluation. Also, this approach can be extended to provide valuable inputs to other advanced methodologies to perform better diagnosis and prognosis for composites.

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“…Using these methods, some authors have considered sensor faults only [7,[11][12][13][14][15], actuator faults only [9,10,[16][17][18][19][20], or non-simultaneous sensor and actuator faults [8,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…There is also an emerging trend towards fully non-linear FDI methods [9, 10]. Using these methods, some authors have considered sensor faults only [7, 11–15], actuator faults only [9, 10, 16–20], or non-simultaneous sensor and actuator faults [8, 21, 22].…”

Section: Introductionmentioning

confidence: 99%

Control-based fault detection and isolation for autonomous aircraft

Marzat

Piet-Lahanier

Damongeot

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part G:

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To cite this version:Julien Marzat, Hélène Piet-Lahanier, Frédéric Damongeot, Eric Walter. Control-based fault detection and isolation for autonomous aircraft. Abstract This paper describes a new method to perform fault detection and isolation for a closedloop-controlled autonomous aircraft. This vehicle is equipped with standard sensors and actuators, and its dynamics is nonlinear. It is assumed that a guidance law and a control loop have been designed to achieve a given mission. The diagnosis method uses the resulting control objectives to generate residuals indicative of the presence of faults. Two classical guidance laws are considered, leading to different control constraints and diagnosis signals. A structural sensitivity analysis shows that all sensor and actuator faults can be detected and all sensor faults isolated, for both laws. The fault diagnosis procedure does not require the costly integration of the model of the system, and the closed-loop scheme makes it robust to model uncertainty. Realistic simulation results with strong model and measurement uncertainty demonstrate the potential of the approach. A theoretical analogy with observer-based fault diagnosis is also derived.

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Fault diagnosis with matrix analysis for electrically actuated unmanned aerial vehicles

Cited by 4 publications

References 32 publications

Statistical process monitoring of a multiphase flow facility

Statistical process monitoring of a multiphase flow facility

Fault tree analysis for composite structural damages

Control-based fault detection and isolation for autonomous aircraft

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