DNN-CCA

Deep neural network aided canonical correlation analysis (DNN-CCA) in Tensorflow and Keras

This repository contains code for case study I in paper

Z. Chen, K. Liang, S. X. Ding, C. Yang, T. Peng and X. Yuan, "A comparative study of deep neural network aided canonical correlation analysis-based process monitoring and fault detection methods," IEEE Transactions on Neural Networks and Learning Systems, 2021, doi:10.1109/TNNLS.2021.3072491.

1 Getting Started

1.1 Installation

Python3.6 and Tensorflow1.15 are required and should be installed on the host machine following the official guide.

1. Clone this repository

git clone https://github.com/CSU-IILab/DNN-CCA

2. Install the required packages

pip install -r requirements.txt

2 Instructions

This repository provides the complete code for building deep neural network aided CCA, and a Jupyter Notebook for model training and testing.

2.1 Model definition

• Model structures are defined in lib/model_xxx.py, no hyperparameter included.

• All the models can be run via lib/run_dcca.py

• The dataset is generated by simple numerical equation of random variables, consistent with the paper.

2.2 Deep neural network aided CCA Notebook

• It's a script to run the Linear CCA and deep neural network aided CCA, it controls the running of the model by executing lib/lcca_detect.py and lib/run_dcca.py.
• All the hyperparameters can be set by using this notebook, as well as train and test the model.

3 Citation

Please cite our paper if you use this code or any of the models.

@article{chen2021dnncca,
  title={A comparative study of deep neural network aided canonical correlation analysis-based process monitoring and fault detection methods},
  author={Chen, Zhiwen and Liang, Ketian and Ding, Steven X and Yang, Chao and Peng, Tao and Yuan, Xiaofeng},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  volume={},
  number={},
  pages={},
  year={},
  publisher={IEEE},
  doi={10.1109/TNNLS.2021.3072491}
}

4 License

MIT License

5 Related works

• Canonical correlation analysis-based fault detection and process monitoring (Matlab source code)

Z. Chen, S. X. Ding, T. Peng, C. Yang and W. Gui, "Fault detection for non-Gaussian process using generalized canonical correlation analysis and randomized algorithms," IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1559-1567, 2018.

• Distributed CCA-based fault detection (Matlab source code)

Z. Chen, Y. Cao, S. X. Ding, K. Zhang, T. Koenings, T. Peng, C. Yang and W. Gui, "A Distributed Canonical Correlation Analysis-Based Fault Detection Method for Plant-Wide Process Monitoring," IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2710-2720, 2019.