Sensor validation using principal component analysis

Kerschen, Gaëtan; Boe, Pascal De; Golinval, Jean‐Claude; Worden, Keith

doi:10.1088/0964-1726/14/1/004

Cited by 134 publications

(66 citation statements)

References 20 publications

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“…In the above references, the threshold associated to the proposed damage indices are defined manually [19], or following the assumption of Gaussian distribution of the damage index [20].…”

Section: Figurementioning

confidence: 99%

Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure

Hajrya

Mechbal

2013

Structural Health Monitoring

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible.This is an author-deposited version published in: https://sam.ensam.eu Handle ID: .http://hdl.handle.net/10985/8199 To cite this version :Rafik HAJRYA, Nazih MECHBAL -Principal component analysis and perturbation theory-based robust damage detection of multifunctional aircraft structure -Structural Health Monitoring -Vol. 12 (, n°3, p.263-277 -2013 Any correspondence concerning this service should be sent to the repository

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“…In the above references, the threshold associated to the proposed damage indices are defined manually [19], or following the assumption of Gaussian distribution of the damage index [20].…”

Section: Figurementioning

confidence: 99%

Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure

Hajrya

Mechbal

2013

Structural Health Monitoring

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“…Sensor validation refers to the detection, isolation, and reconstruction of faulty sensors (Pranatyasto and Qin, 2001;Kerschen et al, 2004). Recently, a large variety of methods for sensor validation have been introduced in the literature; these methods can be grouped into two categories: modeldriven methods and data-driven methods (Rahme and Meskin, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multiple Sensor Validation for Natural Gas Combined Cycle Power Plants Based on Robust Input Training Neural Networks

Ren

2017

J. Chem. Eng. Japan / JCEJ

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Modern industrial power plants require continuous monitoring to ensure their safe and reliable operation. This monitoring depends heavily on accurate sensor readings from a large number of sensors, making sensors an important part of any plant. The possibility of more than a single sensor being simultaneously faulty should not be overlooked. In this paper, a nonlinear system data validation approach based on a robust input-training neural network is proposed for detecting, isolating, and reconstructing multiple sensor faults in a power plant. An in uence factor function and reliability coe cients were introduced into an objective function for the purpose of inhibiting the in uence of numerous failure data with signi cant errors. The proposed method was evaluated on a single-shaft gas turbine from a natural gas combined cycle power plant. The result shows that the proposed model has a high degree of accuracy in multiple sensor failure cases.

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“…Examples of statistical methods that are used for sensor fault detection are principal component analysis (PCA; Kerschen et al, 2005) and linear discriminant analysis (LDA; Helwig et al, 2015). In Kerschen et al (2005) PCA is used to detect sensor faults differences between a reference measurement and live measurements for linear systems.…”

Section: Introductionmentioning

confidence: 99%

“…In Kerschen et al (2005) PCA is used to detect sensor faults differences between a reference measurement and live measurements for linear systems. The approach used in Helwig et al (2015) utilises the data of multiple sensor fault states and a fault-free state to generate an LDA space, which is a space reduced in dimensional-ity that allows for a linear separation between the different fault states and the non-faulty state.…”

Section: Introductionmentioning

confidence: 99%

Sensor defect detection in multisensor information fusion

Ehlenbröker¹,

Mönks²,

Lohweg³

2016

J. Sens. Sens. Syst.

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Abstract. In industrial processes a vast variety of different sensors is increasingly used to measure and control processes, machines, and logistics. One way to handle the resulting large amount of data created by hundreds or even thousands of different sensors in an application is to employ information fusion systems. Information fusion systems, e.g. for condition monitoring, combine different sources of information, like sensors, to generate the state of a complex system. The result of such an information fusion process is regarded as a health indicator of a complex system. Therefore, information fusion approaches are applied to, e.g., automatically inform one about a reduction in production quality, or detect possibly dangerous situations. Considering the importance of sensors in the previously described information fusion systems and in industrial processes in general, a defective sensor has several negative consequences. It may lead to machine failure, e.g. when wear and tear of a machine is not detected sufficiently in advance. In this contribution we present a method to detect faulty sensors by computing the consistency between sensor values. The proposed sensor defect detection algorithm exemplarily utilises the structure of a multilayered group-based sensor fusion algorithm. Defect detection results of the proposed method for different test cases and the method's capability to detect a number of typical sensor defects are shown.

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Sensor validation using principal component analysis

Cited by 134 publications

References 20 publications

Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure

Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure

Multiple Sensor Validation for Natural Gas Combined Cycle Power Plants Based on Robust Input Training Neural Networks

Sensor defect detection in multisensor information fusion

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