Use of NLPCA for sensors fault detection and localization applied at WTP

Bouzenad, Khaled; Ramdani, Messaoud; Zermi, Narima; Mendaci, Khaled

doi:10.1109/wccit.2013.6618761

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Multimode system condition monitoring using sparsity reconstruction for quality control (Wafa Bougheloum) 2715 in which SPE is the overall squared prediction error obtained before the reconstruction and SPE j is the jth squared prediction error obtained after the reconstruction [25]. The validity index for a faulty sensor should tend to zero.…”

Section: Sensor Validity Indexmentioning

confidence: 99%

Multimode system condition monitoring using sparsity reconstruction for quality control

Bougheloum

Bekaik

Gherbi

2023

IJECE

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<p>In this paper, we introduce an improved multivariate statistical monitoring method based on the stacked sparse autoencoder (SSAE). Our contribution focuses on the choice of the SSAE model based on neural networks to solve diagnostic problems of complex systems. In order to monitor the process performance, the squared prediction error (SPE) chart is linked with nonparametric adaptive confidence bounds which arise from the kernel density estimation to minimize erroneous alerts. Then, faults are localized using two methods: contribution plots and sensor validity index (SVI). The results are obtained from experiments and real data from a drinkable water processing plant, demonstrating how the applied technique is performed. The simulation results of the SSAE model show a better ability to detect and identify sensor failures.</p>

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Section: Sensor Validity Indexmentioning

confidence: 99%

Multimode system condition monitoring using sparsity reconstruction for quality control

Bougheloum

Bekaik

Gherbi

2023

IJECE

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“…where the SPE is the global squared prediction error calculated before reconstruction and SPE j is the jth sensor calculated after reconstruction, [33][34][35].…”

Section: Case Study : Wastewater Treatment Plant (Wwtp) Monitoringmentioning

confidence: 99%

Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network

Bouzenad

Ramdani

2017

Algorithms

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Abstract:Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.Keywords: multivariate statistical process control (MSPC); bottleneck neural network (BNN); Gaussian mixture model (GMM); adaptive confidence limit (ACL); wastewater treatment plant (WWTP); benchmark simulation model No. 1 (BSM1)

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“…Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing data [14]. PCA is one of several statistical tools available for reducing the dimensionality of a data set [15]. The extracted feature vectors in the reduced space are used to train the ANN Classifier.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network

Uddin¹,

Yousuf

2015

J. Sci. Res.

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The recognition of human posture from images is currently a very active area of research in computer vision. This paper presents a novel recognition method to determine a human posture is of walking or sitting using Principal Component Analysis (PCA) and Artificial Neural Network (ANN). In this paper, two types of learning are used to recognize the human posture. One is unsupervised and another is supervised learning. We have used PCA for unsupervised learning and ANN for supervised learning. To evaluate the performance of the proposed method, we have considered four types of human posture; walking, sitting, right leg up-down and left leg up-down. The experimental results on the human action of walking, sitting, right leg up-down and left leg up-down database show that our approach produces accurate recognition.

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Use of NLPCA for sensors fault detection and localization applied at WTP

Cited by 4 publications

References 13 publications

Multimode system condition monitoring using sparsity reconstruction for quality control

Multimode system condition monitoring using sparsity reconstruction for quality control

Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network

A New Method for Human Posture Recognition Using Principal Component Analysis and Artificial Neural Network

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