Change detection in a distillation column using non‐linear auto‐regressive moving average with exogenous input model and Hellinger distance

Aggoune, Lakhdar; Chetouani, Yahya; Radjeai, Hammoud

doi:10.1049/iet-smt.2015.0045

Cited by 14 publications

(3 citation statements)

References 29 publications

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“…Specifically, HD metric, which was initially developed by Ernest Hellinger, is an important statistical measure that can be used to quantify the dissimilarity or closeness between two probability density functions (PDFs) [33,31,32]. This measure has been used extensively by scientists and engineers in various disciplines, including pattern recognition [34], image processing [35], classification [36,37], and anomaly detection [38,32,39]. In addition, it has been used effectively within the ecological domain [40], network intrusion detection [39], and fraud detection in insurance applications [41].…”

Section: Hellinger Distance-based Monitorng Schemementioning

confidence: 99%

Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

Harrou

Madakyaru

Sun

2017

Energy and Buildings

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Citation Harrou F, Madakyaru M, Sun Y (2017) Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring. Energy and Buildings. Available: http:// dx.Please cite this article as: Fouzi Harrou, Muddu Madakyaru, Ying Sun, Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring, (2017), http://dx. AbstractFault detection has a vital role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. This paper proposes an innovative multivariate fault detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions obtained using fault-free data. Furthermore, to enhance further the robustness of these methods to measurement noise, and reduce the false alarms due to modeling errors, wavelet-based multiscale filtering of residuals is used before the application of the HD-based monitoring scheme. The performances of the developed NLPLS-HD fault detection technique is illustrated using simulated plug flow reactor data. The results show that the proposed method provides favorable performance for detection of faults compared to the conventional NLPLS method.

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Section: Hellinger Distance-based Monitorng Schemementioning

confidence: 99%

Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

Harrou

Madakyaru

Sun

2017

Energy and Buildings

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“…In fault prediction, Aggoune et al [25] proposed a fault detection method based on nonlinear autoregressive moving average (ARMA) with an exogenous input model and Hellinger distance for a distillation unit. Wu et al [26] used a fractal autoregressive integrated moving average (ARIMA) model to predict the skip-over of a machinery.…”

Section: Introductionmentioning

confidence: 99%

Fault Forecasting of a Machining Center Tool Magazine Based on Health Assessment

Wang

et al. 2020

Mathematical Problems in Engineering

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A tool magazine is one of the key functional components of machining centers with frequent faults. The reliability level of a tool magazine directly affects the reliability level of the machining center. After establishing a reliability test bench and a prognostic and health management system for a tool magazine, a novel fault-forecasting method for machining center tool magazines based on health assessment is proposed. First, the health status of each tool magazine subcomponent is determined using the grey clustering method. Second, the weight of each tool magazine subcomponent is determined using an entropy weight method. Third, the health status of the tool magazine is evaluated via fuzzy comprehensive evaluation. If the tool magazine exhibits an unhealthy status, then the subcomponent with the worst health status is selected for fault forecasting. In addition, standardized treatment, stationarity test, and differential processing are conducted separately on the raw performance indicator data of the worst subcomponent. Finally, the performance indicators of the worst subcomponent are forecasted with the constructed autoregressive moving average model. Using tool-falling failure as an example, the forecasted and experimental tool-pulling forces are compared and analyzed, and the prediction accuracy of the proposed method is verified.

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“…A distillation column has reported by a broad application of the nonlinear dynamic behaviour [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

NARX-based BPSO modelling for time-varying steam temperature of steam distillation pilot plant

Hambali¹,

Taib²,

Yassin³

et al. 2018

J. Fundam and Appl Sci.

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Change detection in a distillation column using non‐linear auto‐regressive moving average with exogenous input model and Hellinger distance

Cited by 14 publications

References 29 publications

Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

Improved nonlinear fault detection strategy based on the Hellinger distance metric: Plug flow reactor monitoring

Fault Forecasting of a Machining Center Tool Magazine Based on Health Assessment

NARX-based BPSO modelling for time-varying steam temperature of steam distillation pilot plant

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