Fault detection and identification of nonlinear processes based on kernel PCA

Choi, Sang Wook; Lee, Changkyu; Lee, Jongmin; Park, Jin Hyun; Lee, In‐Beum

doi:10.1016/j.chemolab.2004.05.001

Cited by 409 publications

(233 citation statements)

References 17 publications

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“…So number of principle components that need to be estimated is also much larger. The KPCA method has exhibited superior performance compared to linear PC analysis method in processing nonlinear systems [7], [8]. The detail introduction of the basic KPCA can be viewed in [7], and [9].…”

Section: Related Workmentioning

confidence: 99%

“…The KPCA method has exhibited superior performance compared to linear PC analysis method in processing nonlinear systems [7], [8]. The detail introduction of the basic KPCA can be viewed in [7], and [9]. Kernel PCA (KPCA), as presented by Scholkopf et al, is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure.…”

Section: Related Workmentioning

confidence: 99%

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Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Ghorbel

Jmal

Abid

et al. 2015

Lecture Notes in Computer Science

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Abstract. In the data mining literature, many outlier detection models can be found. However, these models are not suitable for the energy constrained WSNs because they assumed the whole data is available in a central location for further analysis. In this paper, we propose Distributed and Efficient One-class Outliers Detection Classifier (DEOODC) based on Mahalanobis Kernel used for outlier detection in wireless sensor networks (WSNs). For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. Kernel PCA (KPCA) mapping the data onto another feature space and using nonlinear function. On account of the attractive capability, KPCA-based methods have been extensively investigated, and have showed excellent performance. Within this setting, we propose Kernel Principal Component Analysis based Mahalanobis kernel as a new outlier detection method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate outlier points from normal pattern of data distribution. The use of KPCA based Mahalanobis kernel on real word data obtained from Intel Berkeley are reported showing that the proposed method performs better in finding outliers in wireless sensor networks when compared to the One-Class SVM detection approach. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Ghorbel

Jmal

Abid

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…where Z α is the inverse normal distribution value for the significance level (α/2), (Choi et al, 2005). Samples above Q α do not present robust feature extractions (Conlin et al, 2000).…”

Section: Singular Value Decompositionmentioning

confidence: 99%

Yeast Metabolic State Identification by Fiber Optics Spectroscopy

Castro¹,

Silva²,

Lopes³

et al. 2009

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Abstract:In this manuscript we explore the feasibility of using LWUV-VIS-SWNIR (340 -1100 nm) spectroscopy to classify Saccharomyces cerevisiae colony structures in YP agar and YPD agar, under different growth conditions, such as:Results show that LWUV-VIS-SWNIR spectroscopy has the potential for yeasts metabolic state identification once the spectral signatures of colonies differs from each others, being possible to acheive 100% of classification in UV-VIS and VIS-SWNIR. The UV-VIS region present high discriminant information (350-450 nm), and different responses to UV excitation were obtained. Therefore, high precision is obtained because UV-VIS and VIS-NIR exhibit different kinds of information. In the future, high precision analytical chemistry techniques such as mass spectroscopy and molecular biology transcriptomic studies should be performed in order to understand the detailed cell metabolism and genomic phenomena that characterize the yeast colony state.

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“…More complex PCAbased methods use dynamic PCA-based approaches for sensor fault detection, as for example in Hu et al (2012), where a self-adapting PCA-based method is used. For the detection of sensor defects in non-linear systems, PCA-based methods that use kernel functions are proposed (Choi et al, 2005).…”

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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Fault detection and identification of nonlinear processes based on kernel PCA

Cited by 409 publications

References 17 publications

Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Yeast Metabolic State Identification by Fiber Optics Spectroscopy

Sensor defect detection in multisensor information fusion

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