Nonlinear predictable feature learning with explanatory reasoning for complicated industrial system fault diagnosis

This publication has 41 references indexed in Scilit:

• Sensitivity maps of the Hilbert–Schmidt independence criterion
  Applied Soft Computing, 2018
• Review on data-driven modeling and monitoring for plant-wide industrial processes
  Chemometrics and Intelligent Laboratory Systems, 2017
• Scalable Gaussian Kernel Support Vector Machines with Sublinear Training Time Complexity
  Information Sciences, 2017
• Graph-based predictable feature analysis
  Machine Learning, 2017
• A review of fault detection and diagnostics methods for building systems
  Science and Technology for the Built Environment, 2017
• Financial time series analysis based on effective phase transfer entropy
  Physica A: Statistical Mechanics and its Applications, 2017
• Multimode non‐Gaussian process monitoring based on local entropy independent component analysis
  The Canadian Journal of Chemical Engineering, 2016
• Direction of information flow in large-scale resting-state networks is frequency-dependent
  Proceedings of the National Academy of Sciences, 2016
• Slow Feature Analysis: Unsupervised Learning of Invariances
  Neural Computation, 2002
• A plant-wide industrial process control problem
  Computers & Chemical Engineering, 1993