Model-based and data-driven with multiscale sum of squares double EWMA control chart for fault detection in biological systems

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1 Citation (Scopus)

Abstract

The objectives of this paper will be sought. First, an enhanced technique that can accurately model biological processes will be developed. To deal with scenarios where a process model is available, the particle filter method will be developed to better handle the nonlinear and high-dimensional state estimation problem. Second, a multiscale sum of squares double exponentially weighted moving average (MS-SS-DEWMA) chart will be applied to the monitored residuals in order to enhance the fault detection abilities. The advantage of MS-SS-DEWMA chart is twofold: (1) The SS-DEWMA chart uses the sum of squares statistics; it simultaneously monitors the process mean and variance in a single chart. It has presented better performance than the classical EWMA-based charts. (2) The multiscale data representation can be used as an effective tool for reducing noise from a signal's time series. The effectiveness of the proposed strategy is validated using a synthetic and simulated Cad system in Escherichia coli (CSEC) data. When the simulated CSEC model is used, the developed approach is applied for monitoring some of the key variables involved in the CSEC model. The proposed strategy is also applied to detect diseases using genomic copy number data through better detection of aberrations in the genetic information of patients, which can help medical doctors make more accurate diagnosis of diseases.

Original languageEnglish
JournalJournal of Chemometrics
DOIs
Publication statusAccepted/In press - 1 Jan 2018

Fingerprint

EWMA Chart
Control Charts
Sum of squares
Biological systems
Fault Detection
Fault detection
Chart
Data-driven
Biological Systems
Exponentially Weighted Moving Average
Model-based
Escherichia coli
Escherichia Coli
State estimation
Aberrations
Process Mean
Biological Models
Filter Method
Particle Method
Particle Filter

Keywords

  • Cad system in Escherichia coli (CSEC)
  • Exponentially weighted moving average (EWMA)
  • Fault detection (FD)
  • Genomic copy number data (CND)
  • Multiscale representation
  • Sum of squares double EWMA (SS-DEWMA)

ASJC Scopus subject areas

  • Analytical Chemistry
  • Applied Mathematics

Cite this

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title = "Model-based and data-driven with multiscale sum of squares double EWMA control chart for fault detection in biological systems",
abstract = "The objectives of this paper will be sought. First, an enhanced technique that can accurately model biological processes will be developed. To deal with scenarios where a process model is available, the particle filter method will be developed to better handle the nonlinear and high-dimensional state estimation problem. Second, a multiscale sum of squares double exponentially weighted moving average (MS-SS-DEWMA) chart will be applied to the monitored residuals in order to enhance the fault detection abilities. The advantage of MS-SS-DEWMA chart is twofold: (1) The SS-DEWMA chart uses the sum of squares statistics; it simultaneously monitors the process mean and variance in a single chart. It has presented better performance than the classical EWMA-based charts. (2) The multiscale data representation can be used as an effective tool for reducing noise from a signal's time series. The effectiveness of the proposed strategy is validated using a synthetic and simulated Cad system in Escherichia coli (CSEC) data. When the simulated CSEC model is used, the developed approach is applied for monitoring some of the key variables involved in the CSEC model. The proposed strategy is also applied to detect diseases using genomic copy number data through better detection of aberrations in the genetic information of patients, which can help medical doctors make more accurate diagnosis of diseases.",
keywords = "Cad system in Escherichia coli (CSEC), Exponentially weighted moving average (EWMA), Fault detection (FD), Genomic copy number data (CND), Multiscale representation, Sum of squares double EWMA (SS-DEWMA)",
author = "Majdi Mansouri and Mohamed-Faouzi Harkat and Teh, {Sin Yin} and Ayman Al-khazraji and Hazem Nounou and Mohamed Nounou",
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AU - Mansouri, Majdi

AU - Harkat, Mohamed-Faouzi

AU - Teh, Sin Yin

AU - Al-khazraji, Ayman

AU - Nounou, Hazem

AU - Nounou, Mohamed

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AB - The objectives of this paper will be sought. First, an enhanced technique that can accurately model biological processes will be developed. To deal with scenarios where a process model is available, the particle filter method will be developed to better handle the nonlinear and high-dimensional state estimation problem. Second, a multiscale sum of squares double exponentially weighted moving average (MS-SS-DEWMA) chart will be applied to the monitored residuals in order to enhance the fault detection abilities. The advantage of MS-SS-DEWMA chart is twofold: (1) The SS-DEWMA chart uses the sum of squares statistics; it simultaneously monitors the process mean and variance in a single chart. It has presented better performance than the classical EWMA-based charts. (2) The multiscale data representation can be used as an effective tool for reducing noise from a signal's time series. The effectiveness of the proposed strategy is validated using a synthetic and simulated Cad system in Escherichia coli (CSEC) data. When the simulated CSEC model is used, the developed approach is applied for monitoring some of the key variables involved in the CSEC model. The proposed strategy is also applied to detect diseases using genomic copy number data through better detection of aberrations in the genetic information of patients, which can help medical doctors make more accurate diagnosis of diseases.

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