Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column

Chetouani, Yahya

doi:10.1016/j.psep.2013.02.004

Cited by 31 publications

(15 citation statements)

References 24 publications

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“…[14][15][16][17] The multivariate detection techniques include latent variable-based techniques, which are the well-known empirical data-based techniques including partial least squares (PLS) and PCA. [18][19][20][21] The univariate techniques such as cumulative sum (CUSUM), Shewhart, 22 exponentially weighted moving average chart (EWMA), 23,24 and generalized likelihood ratio (GLR) test 25,26 have been developed.…”

Section: Introductionmentioning

confidence: 99%

New sensor fault detection and isolation strategy–based interval‐valued data

Harkat

Mansouri

Abodayeh

et al. 2020

Journal of Chemometrics

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In this paper, a new data-driven sensor fault detection and isolation (FDI) technique for interval-valued data is developed. The developed approach merges the benefits of generalized likelihood ratio (GLR) with interval-valued data and principal component analysis (PCA). This paper has three main contributions. The first contribution is to develop a criterion based on the variance of interval-valued reconstruction error to select the number of principal components to be kept in the PCA model. Secondly, interval-valued residuals are generated, and a new fault detection chart-based GLR is developed. Lastly, an enhanced interval reconstruction approach for fault isolation is developed. The proposed strategy is applied for distillation column process monitoring and air quality monitoring network. KEYWORDSdata-driven process monitoring, fault detection and isolation, generalized likelihood ratio, interval-valued data, principal component analysis, reconstruction INTRODUCTIONModern chemical plants are becoming increasingly complex, making the need for enhanced process safety, consistent high product quality, and operational efficiency very crucial. These challenges can be addressed by using effective real-time process monitoring methods for abnormal process conditions.Various data-driven process monitoring approaches, such as principal component analysis (PCA), have been used in various industrial applications. 1-3 The application of multivariate approaches for abnormal events detection has been developed mainly in the area of process monitoring. [4][5][6][7][8][9][10][11] The process monitoring scheme involves building a PCA model of the system under the normal operating condition (NOC). The identification of the PCA model relies on estimating the process structure by using an eigendecomposition problem. 3 Several extensions of the PCA model with different applications were proposed in literature to deal with dynamic and nonlinear behavior of processes. 3,9,12 Abnormal situations that occur due to sensor drifts induce changes in sensor measurements. PCA is used to model normal process behavior, and faults are then detected by checking the observed behavior against this model. 6,12 Hotelling's T 2 and squared prediction error SPE statistic are two statistics commonly used in data-driven process monitoring methods. 3,10,13 Regarding fault detection, several univariate and multivariate techniques have been developed in literature for process monitoring. [14][15][16][17] The multivariate detection techniques include latent variable-based techniques, which are the well-known empirical data-based techniques including partial least squares (PLS) and PCA. [18][19][20][21] The univariate Journal of Chemometrics. 2020;34:e3222.wileyonlinelibrary.com/journal/cem

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Section: Introductionmentioning

confidence: 99%

New sensor fault detection and isolation strategy–based interval‐valued data

Harkat

Mansouri

Abodayeh

et al. 2020

Journal of Chemometrics

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“…On the other hand, usually, once a physics-based model is established, the relevant model's parameters cannot be adjusted in real-time, which means that the actual degeneration in running states as reflected in the observed vibration signals will not be used. In contrast, data-driven prediction models such as the Bayesian method [7,8], machine learning techniques [9,10], support vector machine [11,12], etc., which utilize the measured historical data to establish the failure degradation model and avoid merging all kinds of physical knowledge of the system allow for an adjustment of the model parameters to capture the degradation trend of vibration signals. However, the common drawback of data-driven approaches is that they do not establish a theoretical mechanism between the physical degradation state (e.g., defect size) and the parameters obtained from the prediction models.…”

Section: Introductionmentioning

confidence: 99%

Degradation Trend Prediction for Rotating Machinery Using Long-Range Dependence and Particle Filter Approach

Liang

2018

Algorithms

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Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive-moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc.

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“…Other univariate‐ and multivariate‐based failure detection techniques have been developed for PV systems monitoring . The multivariate detection techniques are mainly based on partial least squares (PLS) and principal component analysis (PCA), while the univariate techniques include cumulative sum, Shewhart, and exponentially weighted moving average (EWMA) charts . In previous studies, the authors have proved that the classical GLRT chart–based equal variances present a better detection efficiency against the EWMA and Shewhart statistics, which is due to the ability of the GLRT to minimize the false alarm rate (FAR).…”

Section: Introductionmentioning

confidence: 99%

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

Mansouri

Hajji

Trabelsi

et al. 2018

Int Trans Electr Energ Syst

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Summary In this paper, a new multiscale weighted generalized likelihood ratio test (MS‐WGLRT) chart is proposed for enhanced failure detection in photovoltaic systems. The main weakness of the classical generalized likelihood ratio test chart is in dealing with residual samples while ignoring their natural variances. By taking into consideration the nature variance of the detection residual and applying a multiscale representation, the proposed technique allows the reduction in false alarm and missed detection rates compared with the classical generalized likelihood ratio test chart. The multiscale representation of data is an efficient data analysis and feature extraction tool that has a great impact on the effectiveness of failure detection. The effectiveness of the proposed method is evaluated on a simulated photovoltaic data where the developed chart is used for detecting single and multiple failures (eg, bypass, mix, and shading failures). The simulation results show that the multiscale weighted generalized likelihood ratio test method offers better performance compared with the classical generalized likelihood ratio chart.

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Model selection and fault detection approach based on Bayes decision theory: Application to changes detection problem in a distillation column

Cited by 31 publications

References 24 publications

New sensor fault detection and isolation strategy–based interval‐valued data

New sensor fault detection and isolation strategy–based interval‐valued data

Degradation Trend Prediction for Rotating Machinery Using Long-Range Dependence and Particle Filter Approach

Enhanced generalized likelihood ratio test for failure detection in photovoltaic systems

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