Sensor fault detection and isolation of an autonomous underwater vehicle using partial kernel PCA

Navi, Mania; Davoodi, Mohammadreza; Meskin, Nader

doi:10.1109/icphm.2015.7245022

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The kernel PCA introduces a nonlinear PCA approach for the input space [26]. If a PCA is utilized to decouple nonlinear correlations of a given data set x n , namely, for n ∈ {1, .…”

Section: Principlementioning

confidence: 99%

“…where N ∑ j=1 ∅ x j = 0 and ∅ expresses a nonlinear mapping function that projects the input vectors from the input space into F. Note that the dimension of this feature space can be possibly infinite or else arbitrarily large [26]. To diagonalize the covariance matrix, the eigen-value problem in this feature space needs to be solved as:…”

Section: Principlementioning

confidence: 99%

“…Our review of the literature has established dual types of nonlinear PCA. The first relies on neural networks to compensate for data nonlinearity, while the second type is the kernel PCA [22][23][24][25][26].…”

Section: Introduction and Bibliographical Reviewmentioning

confidence: 99%

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Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

et al. 2022

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In this study, a multiscale monitoring method for nonlinear processes was developed. We introduced a machine learning tool for fault detection and isolation based on the kernel principal component analysis (PCA) and discrete wavelet transform. The principle of our proposal involved decomposing multivariate data into wavelet coefficients by employing the discrete wavelet transform. Then, the kernel PCA was applied on every matrix of coefficients to detect defects. Only those scales that manifest overruns of the squared prediction errors in control limits were considered in the data reconstruction phase. Thus, the kernel PCA was approached on the reconstructed matrix for detecting defects and isolation. This approach exploits the kernel PCA performance for nonlinear process monitoring in combination with multiscale analysis when processing time-frequency scales. The proposed method was validated on a photovoltaic system related to a complex industrial process. A data matrix was determined from the variables that characterize this process corresponding to motor current, angular speed, convertor output voltage, and power voltage system output. We tested the developed methodology on 1000 observations of photovoltaic variables. A comparison with monitoring methods based on neural PCA was established, proving the efficiency of the developed methodology.

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“…The kernel PCA introduces a nonlinear PCA approach for the input space [26]. If a PCA is utilized to decouple nonlinear correlations of a given data set x n , namely, for n ∈ {1, .…”

Section: Principlementioning

confidence: 99%

Section: Principlementioning

confidence: 99%

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Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

et al. 2022

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Data‐driven approach for fault detection and isolation in nonlinear system

Kallas

Mourot

Maquin

et al. 2018

Adaptive Control & Signal

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SummarySystem diagnosis has been of a great interest for all aspects of industrial processes more precisely to gain in quality. It is based essentially on the analysis of the links between the variables of a system and more precisely on the changes of the relations between these variables, which testify to the presence of faults or anomalies. For that purpose, data modeling is the process of finding a mathematical expression that provides a good fit between given finite sample values of the independent variables and the associated values of the dependent variables of the process.The aim of this paper is to detect and, above all, localize faults affecting a system with nonlinear behavior, when its model is not known a priori. An important part of the presentation is dedicated to the construction of fault indicators capable of locating faults, ie, recognizing the input or output of a system affected by a fault. The first part of this paper is devoted to how to predict the output of a nonlinear behavior system. The second part proposes a way for the detection and isolation of measurement faults based on the proposed prediction model. The relevance of the proposed technique, for modeling and system diagnosis, is illustrated on a simulated example in the context of SIMO and MIMO systems.

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Fault diagnosis and self-healing for smart manufacturing: a review

Aldrini,

Chihi,

Sidhom

2023

J Intell Manuf

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Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to find and detect any potential anomalies and faults as soon as possible because of the low tolerance for performance deterioration, productivity decline, and safety issues. To overcome these issues, a variety of approaches exist in the literature. However, the multitude of techniques make it difficult to choose the appropriate method in relation to a given context. This paper proposes a new architecture for a conceptual model of intelligent fault diagnosis and self-healing for smart manufacturing systems. Based on this architecture, a review method for the different approaches, sub-approaches and methods used to develop a Fault Detection and Diagnosis (FDD) and Self-Healing-Fault-Tolerant (SH-FT) strategy dedicated to smart manufacturing is defined. Moreover, this paper reviews and analyzes more than 256 scientific articles on fault diagnosis and self-healing approaches and their applications in SM in the last decade. Finally, promising research directions in the field of resilient smart manufacturing are highlighted.

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Sensor fault detection and isolation of an autonomous underwater vehicle using partial kernel PCA

Cited by 6 publications

References 28 publications

Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

Data‐driven approach for fault detection and isolation in nonlinear system

Fault diagnosis and self-healing for smart manufacturing: a review

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