Determination of principal component analysis models for sensor fault detection and isolation

Benaicha, Anissa; Mourot, Gilles; Benothman, Kamel; Ragot, José

doi:10.1007/s12555-012-0142-x

Cited by 24 publications

(5 citation statements)

References 12 publications

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“…The data is projected in the highest variance direction. Outliers are data points that deviate from these rules [44,53,63,[131][132][133][134][135].…”

Section: Principle Component Analysismentioning

confidence: 99%

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Sharma,

Gupta,

Gupta

et al. 2024

CMES

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The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system's reliability and undermine the trustworthiness of smart environments. To address these concerns, various techniques and algorithms can be employed to enhance the performance of IoT devices through effective fault detection. This paper conducted a thorough review of the existing literature and conducted a detailed analysis. This analysis effectively links sensor errors with a prominent fault detection technique capable of addressing them. This study is innovative because it paves the way for future researchers to explore errors that have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essential factors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and their corresponding recommended techniques. Additionally, the paper presents a methodical overview of fault detection techniques employed in smart devices, including the metrics used for evaluation. Furthermore, the paper examines the body of academic work related to sensor faults and fault detection techniques within the domain. This reflects the growing inclination and scholarly attention of researchers and academicians toward strategies for fault detection within the realm of the Internet of Things.

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“…The data is projected in the highest variance direction. Outliers are data points that deviate from these rules [44,53,63,[131][132][133][134][135].…”

Section: Principle Component Analysismentioning

confidence: 99%

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Sharma,

Gupta,

Gupta

et al. 2024

CMES

View full text Add to dashboard Cite

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“…MPCA reduces the dimensions of the X matrix (K × N), where there are K input variables and N observations in the X matrix, into a lower dimension latent vector space. [13,14,[23][24][25][26] The latent vector space represents a new coordination system determined by projecting the original noisy and collinear data into a reduced space, which contains most of the relevant information about the process. MPCA provides a simpler description of the data variability than the original data.…”

Section: Multiway Principal Component Analysis (Mpca)mentioning

confidence: 99%

Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces

et al. 2020

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This paper describes the development of “heats” and input variables selection models that are incorporated into a water detection framework for an industrial steelmaking electric arc furnace (EAF). The selection models in this work are developed based on latent variable methods. The latent variable methods used in this work are multiway principal component analysis (MPCA) and multiway projection to latent structures (MPLS). The particular problems related to latent variable methods discussed in this paper include data preprocessing, including alignment, unfolding method, centering, and scaling. The outcome of the heats selection model is heats with normal operation and the outcome of the input variables selection model is variables that are highly correlated with the off‐gas water vapour. The water detection framework and developed models are useful in practical settings for the prediction of water leakage and the design of appropriate fault detection and diagnosis strategies.

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“…Among various strategies of fault localization, the variable reconstruction approach is adopted in this application [13,19,21]. This method assumes that each variable is faulty and suggests to reconstruct it using the PCA model from the remaining variables [15,22].…”

Section: Fault Isolationmentioning

confidence: 99%

Spacecraft Actuator Diagnosis with Principal Component Analysis: Application to the Rendez-Vous Phase of the Mars Sample Return Mission

Nasri

Gueddi

Dague

et al. 2015

Journal of Control Science and Engineering

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This paper presents a fault detection and isolation (FDI) approach in order to detect and isolate actuators (thrusters and reaction wheels) faults of an autonomous spacecraft involved in the rendez-vous phase of the Mars Sample Return (MSR) mission. The principal component analysis (PCA) has been adopted to estimate the relationships between the various variables of the process. To ensure the feasibility of the proposed FDI approach, a set of data provided by the industrial “high-fidelity” simulator of the MSR and representing the opening (resp., the rotation) rates of the spacecraft thrusters (resp., reaction wheels) has been considered. The test results demonstrate that the fault detection and isolation are successfully accomplished.

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Determination of principal component analysis models for sensor fault detection and isolation

Cited by 24 publications

References 12 publications

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

Heats and input variables selection for designing a water detection framework applicable to industrial electric arc furnaces

Spacecraft Actuator Diagnosis with Principal Component Analysis: Application to the Rendez-Vous Phase of the Mars Sample Return Mission

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