Sensor Fault Detection and Isolation by Robust Principal Component Analysis

Tharrault, Yvon; Harkat, Mohamed-Faouzi; Mourot, Gilles; Ragot, José

doi:10.5772/9086

Cited by 8 publications

(4 citation statements)

References 34 publications

(40 reference statements)

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“…Once the PCA model identification is achieved, it is necessary to proceed to the fault detection step. Several indices are used to represent any variations in the data and thereby to detect faults either in the principal subspace, in the residual one, or in both spaces [13,18,19].…”

Section: Fault Detection and Isolationmentioning

confidence: 99%

“…After achieving the fault detection step, it is necessary to identify and isolate the faulty variables. 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%

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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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Section: Fault Detection and Isolationmentioning

confidence: 99%

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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show abstract

“…Various approaches to robust PCA have been proposed over the past few decades. − They can be classified into six groups: − (1) taking robust covariance estimators instead of the empirical covariance matrix, , (2) projection pursuit techniques, , (3) a combination of the ideas of groups 1 and 2, (4) robust subspace estimation, (5) elliptical and spherical PCA, and (6) low-rank matrix approximation . Although robust PCA methods have been widely investigated, there are few applications of robust PCA to process monitoring. , In practice, data collected in industrial processes frequently contain outliers. The faulty data, low-quality data, and data obtained during irregular operating phases (e.g., startup or shutdown periods) can all be viewed as outliers. , Therefore, any data-driven process monitoring scheme should take into account the possible presence of outliers in the training data.…”

Section: Introductionmentioning

confidence: 99%

“…Although robust PCA methods have been widely investigated, there are few applications of robust PCA to process monitoring. , In practice, data collected in industrial processes frequently contain outliers. The faulty data, low-quality data, and data obtained during irregular operating phases (e.g., startup or shutdown periods) can all be viewed as outliers. , Therefore, any data-driven process monitoring scheme should take into account the possible presence of outliers in the training data. For the process monitoring, outliers affect not only the monitoring model but also the fault detection indices and their control limits.…”

Section: Introductionmentioning

confidence: 99%

Sparse Robust Principal Component Analysis with Applications to Fault Detection and Diagnosis

Luo

Bao

Tong

2019

Ind. Eng. Chem. Res.

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Principal component analysis (PCA) is a popular method for modeling and analysis of high-dimensional data. In spite of its advantages, classical PCA also has two drawbacks. First, it is very sensitive to outliers in the data. Second, it cannot yield interpretable PCs because most of the loadings are nonzero. To overcome these drawbacks, we propose a new PCA method that has the properties of robustness and sparsity at the same time, called sparse robust PCA (SRPCA). The robustness is achieved by taking a robust covariance matrix instead of the classical covariance matrix used in PCA. Meanwhile, an additional penalty is imposed on the number of nonzero loadings to achieve the sparsity. SRPCA is not only robust against outliers, but also can yield interpretable PCs. A robust process monitoring method is developed using SRPCA. A cumulative percent contribution criterion is proposed for selecting the optimal PCs for process monitoring. The selected PCs are used to define two fault detection indices. Based on the sparsity of PCs, two-level contribution plots are developed for fault diagnosis. This fault diagnosis method narrows down the faulty variables to active variables (with nonzero loadings) in a dominant PC contributing the most to a fault. The effectiveness and advantages of the proposed method are illustrated with a case study.

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A new interval diagnosis method: Application to the spacecraft rendezvous phase of the Mars sample return mission

Gueddi

Nasri

Othman

2019

Adaptive Control & Signal

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SummaryThis paper presents a new optimized interval principal component analysis applied to detect and isolate actuators faults of an autonomous spacecraft involved in the rendezvous phase of the Mars sample return mission. Based on the exploitation of various arithmetic and interval analysis properties, the new interval model is built by solving the interval eigenpairs problem via a resolution of a parametric linear programming problem. The detection and isolation phases are performed by extending the classic methods to interval‐valued data. The proposed method is applied to detect and isolate actuators faults that can occur on the spacecraft's thrusters. Based on data provided by a “high fidelity” industrial simulator developed by Thales Alenia Space, the obtained results proved the effectiveness of the proposed interval fault diagnosis method on detecting and isolating thrusters' faults.

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Sensor Fault Detection and Isolation by Robust Principal Component Analysis

Cited by 8 publications

References 34 publications

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

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

Sparse Robust Principal Component Analysis with Applications to Fault Detection and Diagnosis

A new interval diagnosis method: Application to the spacecraft rendezvous phase of the Mars sample return mission

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