Improved data-based fault detection strategy and application to distillation columns

Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying

doi:10.1016/j.psep.2017.01.017

Cited by 55 publications

(19 citation statements)

References 51 publications

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“…Traffic data gathered from sensors are usually noisy which makes congestion detection more difficult. The wavelet-based multiscale filter can provide effective noise/feature separation [27]. The novelty of our approach is to develop a robust congestion detection approach to noise measurements by exploiting the benefits of the multiscale representation of data and those of the KLD-EWMA scheme to better detect abnormal congestions.…”

Section: B Combination Of Kld and Ewma Scheme For Congestion Detectionmentioning

confidence: 99%

“…The results from the KLD-EWMA scheme with (ν = 0.25 and κ = 3 ) are shown in Figure 9(b). Decision threshold has been computed using (27). In this example, the proposed scheme detected this abnormal congestion with FAR=1.17% and MDR=4%.…”

Section: Case Studymentioning

confidence: 99%

“…However, traffic data collected from sensors are usually noisy, which makes the congestion detection more challenging as the noisy data degrades detection performances. Multiscale filtering using wavelets is a powerful technique for features/noise separation [27], [28]. The objective is to exploit the advantages of both a multiscale filtering based on wavelet and of KLD-EWMA scheme to better detect congestions and to deal with the problem of measurement noise in the data.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Model-Based Observer and Kullback–Leibler Metric for Estimating and Detecting Road Traffic Congestion

et al. 2018

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Section: B Combination Of Kld and Ewma Scheme For Congestion Detectionmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating Model-Based Observer and Kullback–Leibler Metric for Estimating and Detecting Road Traffic Congestion

et al. 2018

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“…, n is normally distributed with mean µ and covariance matrix Σ. A multivariate Shewhart chart, also known as a T 2 chart or a χ 2 chart [11], [12], to monitor the process mean is based on the decision statistic:…”

Section: Features Extraction For Lccdmentioning

confidence: 99%

Monitoring land-cover changes by combining a detection step with a classification step

Harrou

Zerrouki

Sun

et al. 2018

2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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CitationHarrou F, Zerrouki N, Sun Y, Hocini L (2018) Monitoring landcover changes by combining a detection step with a classification step. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.Abstract-An approach merging the Hotelling T 2 control scheme with weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. Hotelling T 2 procedure is introduced to identify features corresponding to changed areas. However, T 2 scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for unbalanced problems, has been successfully applied on features of the detected pixels to recognize the type of change. The performance of the algorithm is evaluated using SZTAKI AirChange benchmark data, results show that the proposed detection scheme succeeds to appropriately identify changes to land cover. Also, we compared the proposed approach to that of the conventional algorithms (i.e., neural network, random forest, support vector machine and k-nearest neighbors) and found improved performance.

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“…Unfortunately, the effectiveness of model-based fault-detection approaches relies on the accuracy of the models used. When there is no process model, model-free or process-history-based methods were successfully used in process monitoring because they can effectively deal with highly correlated process variables [8,9]. Such methods require a minimal a prior knowledge about Uncertainty Quantification and Model Calibration process physics, but depends on the availability of quality input data.…”

Section: Introductionmentioning

confidence: 99%

An Improved Wavelet‐Based Multivariable Fault Detection Scheme

Harrou¹,

Sun²,

Madakyaru³

2017

Uncertainty Quantification and Model Calibration

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Data observed from environmental and engineering processes are usually noisy and correlated in time, which makes the fault detection more difficult as the presence of noise degrades fault detection quality. Multiscale representation of data using wavelets is a powerful feature extraction tool that is well suited to denoising and decorrelating time series data. In this chapter, we combine the advantages of multiscale partial least squares (MSPLSs) modeling with those of the univariate EWMA (exponentially weighted moving average) monitoring chart, which results in an improved fault detection system, especially for detecting small faults in highly correlated, multivariate data. Toward this end, we applied EWMA chart to the output residuals obtained from MSPLS model. It is shown through simulated distillation column data the significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional partial least square (PLS)-based Q and EWMA methods and MSPLS-based Q method.

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Improved data-based fault detection strategy and application to distillation columns

Cited by 55 publications

References 51 publications

Integrating Model-Based Observer and Kullback–Leibler Metric for Estimating and Detecting Road Traffic Congestion

Integrating Model-Based Observer and Kullback–Leibler Metric for Estimating and Detecting Road Traffic Congestion

Monitoring land-cover changes by combining a detection step with a classification step

An Improved Wavelet‐Based Multivariable Fault Detection Scheme

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