A deep belief network based fault diagnosis model for complex chemical processes

No abstract is available for this paper.

• Publication year

  2017

• Venue

  Computers and Chemical Engineering

• Publication date

  2017-12-05

• Fields of study

  Chemistry, Engineering, Computer Science

• Identifiers
  DOI 10.1016/j.compchemeng.2017.02.041
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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  2013cited by this paper
• A Practical Guide to Training Restricted Boltzmann Machines

  2012influential reference
• Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods

  2012cited by this paper
• A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

  2012cited by this paper
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  2011cited by this paper
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  2011cited by this paper
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  2011cited by this paper
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  2009cited by this paper
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  2009cited by this paper
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  2009cited by this paper
• Fault Detection of Non-Linear Processes Using Kernel Independent Component Analysis

  2008cited by this paper
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  2008cited by this paper
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  2008cited by this paper
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  2008cited by this paper
• Multivariate Temporal Data Analysis Using Self-Organizing Maps. 1. Training Methodology for Effective Visualization of Multistate Operations

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  2008cited by this paper
• Plant-wide detection and diagnosis using correspondence analysis☆

  2007cited by this paper
• Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors

  2007cited by this paper
• Fault diagnosis using dynamic trend analysis: A review and recent developments

  2007cited by this paper
• Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes

  2007cited by this paper
• Supporting Online Material for Reducing the Dimensionality of Data with Neural Networks

  2006cited by this paper
• Greedy Layer-Wise Training of Deep Networks

  2006cited by this paper
• A Fast Learning Algorithm for Deep Belief Nets

  2006cited by this paper
• Neural network systems for multi-dimensional temporal pattern classification

  2005cited by this paper
• Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process

  2005cited by this paper
• Fault identification for process monitoring using kernel principal component analysis

  2005cited by this paper
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  2003cited by this paper
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  2001cited by this paper
• Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

  2000cited by this paper
• Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis

  2000cited by this paper
• The "wake-sleep" algorithm for unsupervised neural networks.

  1995cited by this paper
• Applications of multivariate statistical methods to process monitoring and controller design

  1994cited by this paper
• A plant-wide industrial process control problem

  1993cited by this paper
• Multivariate statistical monitoring of process operating performance

  1991cited by this paper
• A neural network methodology for process fault diagnosis

  1989influential reference
• Information Processing in Dynamical Systems: Foundations of Harmony Theory

  1987cited by this paper
• Information processing in dynamical systems: foundations of harmony theory

  1986cited by this paper
• Neural networks and physical systems with emergent collective computational abilities.

  1982cited by this paper

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