An improved PCA scheme for sensor FDI: Application to an air quality monitoring network

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

doi:10.1016/j.jprocont.2005.09.007

Cited by 139 publications

(55 citation statements)

References 13 publications

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“…Statistics-based methods have been widely applied in detecting outliers in environmental data (Harkat et al, 2006;Harrou et al, 2013) and process monitoring (Li et al, 2000). The most popular statistics are:…”

Section: Statistics-based Methodsmentioning

confidence: 99%

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Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

Bhushan

Sharker

Karimi

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Realtime detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

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Section: Statistics-based Methodsmentioning

confidence: 99%

“…Most PCA-based models for outlier detection operate in batch mode (Chatzigiannakis and Papavassiliou, 2007;Harrou et al, 2013;Harkat et al, 2006), where the model is first trained using training data and is then used to test the remaining data for outliers. As such, these models are time invariant.…”

Section: Introductionmentioning

confidence: 99%

Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

Bhushan

Sharker

Karimi

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…, N 1 is the number of sensors) after the reconstruction of all sensors. It is noted that the reconstruction of the faulty sensors eliminates the abnormal condition; we must also remember that the jth indicator SPE j of the faulty sensor calculated after reconstruction is below its adaptive upper control limit SPE j (k) < AUCL compared to the SPE j (k) of other variables, which are all above their respective AUCL j [32].…”

Section: Sensor Validity Indexmentioning

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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“…Moreover in the FDI field, Gertler & McAvoy (1997) have shown a close link between PCA and parity space method. Principal component analysis (PCA) has then been applied successfully in the monitoring of complex systems (Chiang & Colegrove, 2007;Harkat et al, 2006;Kano & Nakagawa, 2008).…”

Section: Introductionmentioning

confidence: 99%

Sensor Fault Detection and Isolation by Robust Principal Component Analysis

Tharrault¹,

Harkat²,

Mourot³

et al. 2010

Fault Detection

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An improved PCA scheme for sensor FDI: Application to an air quality monitoring network

Cited by 139 publications

References 13 publications

Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network

Sensor Fault Detection and Isolation by Robust Principal Component Analysis

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