An Intelligent Fault Detection Method Based on Sparse Auto-Encoder for Industrial Process Systems: A Case Study on Tennessee Eastman Process Chemical System. Ren, H., Chai, Y., Qu, J., Zhang, K., & Tang, Q. In 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), volume 01, pages 190–193, August, 2018. doi abstract bibtex This paper introduced a deep learning approach to achieve fault detection with signal analysis and processing, which is based on an sparse auto-encoder and can be employed to achieve unsupervised learning to automatically extract features of complex data-sets to detect fault. This sparse auto-encoder can be employed to extract features from the unrecognized signals to achieve intelligent identification. The hidden layer of auto-encoder can be considered as an over-complete dictionary, which can be employed to reconstruct the input signals to extract data-sets features unsupervised. Furthermore, the sparse auto-encoder can be considered as the method to build up a specific architecture to describe the process industrial system, not only to avoid the requirement of large amount of data onto the training step, but also to handle the problem with small sample training data. Finally, the application of this method of Tennessee Eastman Process Chemical system can be employed to demonstrate and illustrate the effectiveness and the reliability of this proposed method, and the results have shown its excellent performance on fault detection for process industrial systems.
@inproceedings{ren_intelligent_2018,
title = {An {Intelligent} {Fault} {Detection} {Method} {Based} on {Sparse} {Auto}-{Encoder} for {Industrial} {Process} {Systems}: {A} {Case} {Study} on {Tennessee} {Eastman} {Process} {Chemical} {System}},
volume = {01},
shorttitle = {An {Intelligent} {Fault} {Detection} {Method} {Based} on {Sparse} {Auto}-{Encoder} for {Industrial} {Process} {Systems}},
doi = {10.1109/IHMSC.2018.00051},
abstract = {This paper introduced a deep learning approach to achieve fault detection with signal analysis and processing, which is based on an sparse auto-encoder and can be employed to achieve unsupervised learning to automatically extract features of complex data-sets to detect fault. This sparse auto-encoder can be employed to extract features from the unrecognized signals to achieve intelligent identification. The hidden layer of auto-encoder can be considered as an over-complete dictionary, which can be employed to reconstruct the input signals to extract data-sets features unsupervised. Furthermore, the sparse auto-encoder can be considered as the method to build up a specific architecture to describe the process industrial system, not only to avoid the requirement of large amount of data onto the training step, but also to handle the problem with small sample training data. Finally, the application of this method of Tennessee Eastman Process Chemical system can be employed to demonstrate and illustrate the effectiveness and the reliability of this proposed method, and the results have shown its excellent performance on fault detection for process industrial systems.},
booktitle = {2018 10th {International} {Conference} on {Intelligent} {Human}-{Machine} {Systems} and {Cybernetics} ({IHMSC})},
author = {Ren, Hao and Chai, Yi and Qu, Jianfeng and Zhang, Ke and Tang, Qiu},
month = aug,
year = {2018},
keywords = {Chemicals, Dictionaries, Fault Detection, Fault detection, Feature extraction, Process control, Soft-max Classifier, Sparse Auto-Encoder, Tennessee Eastman Process System, Training},
pages = {190--193},
}
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