Quality Monitoring Using Principal Component Analysis and Fuzzy Logic Application in Continuous Casting Process

Bouhouche, Salah; Lahreche, M.; Moussaoui, Abdelkrim; Bast, Jürgen

doi:10.3844/ajassp.2007.637.644

Cited by 20 publications

(11 citation statements)

References 11 publications

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“…There are several methods which are used to find outliers in low dimensional space including Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) [3,9,10]. To process data with a high dimension, PCA has proven to be the preferred option.…”

Section: Related Workmentioning

confidence: 99%

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User activities outlier detection system using principal component analysis and fuzzy rule-based system

Mahmoud

Lotfi

Langensiepen

2012

Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments

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In this paper, a user activities outlier detection system is introduced. The proposed system is implemented in a smart home environment equipped with appropriate sensory devices. An activity outlier detection system consist of a twostage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based System (FRBS). In the first stage, the Hamming distance is used to measure the distances between the activities. PCA is then applied to the distance measures to find two indices of Hotelling's T 2 and Squared Prediction Error (SPE). In the second stage of the process, the calculated indices are provided as inputs to FRBSs to model them heuristically. They are used to identify the outliers and classify them. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers and helps in distinguishing between the normal and abnormal behaviour patterns of the Activities of Daily Living (ADL).

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Section: Related Workmentioning

confidence: 99%

“…The sub spaces spanned by X and X T are called the score space and residual space respectively [9]. The limit for SPE index which denotes the upper control limit for SPE index with a confidence level α = 95% is:…”

Section: Square Prediction Error Statisticmentioning

confidence: 99%

User activities outlier detection system using principal component analysis and fuzzy rule-based system

Mahmoud

Lotfi

Langensiepen

2012

Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments

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“…It is calculated using the following expression:

r MathClass-rel= X^{T} MathClass-bin- X MathClass-rel= X^{T} MathClass-bin- V_{k} V_{k}^{T} X

and

SPE MathClass-rel= r^{T} r

The residual matrix r captures the variations in the observation space spanned by the PCs associated with the M smallest singular values. The subspaces spanned by X and X T are called the score space and residual space, respectively (Bouhouche et al ). The limit for SPE index which denotes the upper control limit for SPE index with a confidence level α = 95 % is (Liu et al ):

SP E_{\lim} MathClass-rel= θ_{1} {([], \frac{C_{a} h_{0} \sqrt{2 θ_{2}}}{θ_{1}} MathClass-bin+ 1 MathClass-bin+ \frac{θ_{2} h_{0} (h_{0} MathClass-bin- 1)}{θ_{1}^{2}})}^{\frac{1}{h_{0}}}

where

θ_{i} MathClass-rel= {MathClass-op\sum}_{j MathClass-rel= k MathClass-bin+ 1}^{m} λ_{j}^{i}

and

h_{0} MathClass-rel= 1 MathClass-bin- \frac{2 θ_{1} θ_{3}}{3 θ_{2}^{2}}

.…”

Section: User Activities Outliers Detectionmentioning

confidence: 99%

User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques

Mahmoud

Lotfi

Langensiepen

2014

Computational Intelligence

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In this paper, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based Systems (FRBS). In the first stage, the Hamming distance is used to measure the differences between different activities. PCA is then applied to the distance measures to find two indices of Hotelling's T 2 and Squared Prediction Error. In the second stage of the process, the calculated indices are provided as inputs to the FRBSs to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioural patterns.

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“…Intelligent methods including the fuzzy method and expert systems (David & Dursun, 2008;Min, Degang, Cheng, & Hongxing, 2006;Bouhouche, Lahreche, & Bast, 2007).…”

Section: Introductionmentioning

confidence: 99%

Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

Bouhouche¹,

Yazid²,

Sissaoui³

et al. 2010

Control Engineering Practice

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Quality Monitoring Using Principal Component Analysis and Fuzzy Logic Application in Continuous Casting Process

Cited by 20 publications

References 11 publications

User activities outlier detection system using principal component analysis and fuzzy rule-based system

User activities outlier detection system using principal component analysis and fuzzy rule-based system

User Activities Outliers Detection; Integration of Statistical and Computational Intelligence Techniques

Evaluation using online support-vector-machines and fuzzy reasoning. Application to condition monitoring of speeds rolling process

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