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18. Hierarchical Deep Recurrent Neural Network based Method for Fault Detection and Diagnosis

Author

Agarwal, Piyush, Gonzalez, Jorge Ivan Mireles, Elkamel, Ali, and Budman, Hector

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

A Deep Neural Network (DNN) based algorithm is proposed for the detection and classification of faults in industrial plants. The proposed algorithm has the ability to classify faults, especially incipient faults that are difficult to detect and diagnose with traditional threshold based statistical methods or by conventional Artificial Neural Networks (ANNs). The algorithm is based on a Supervised Deep Recurrent Autoencoder Neural Network (Supervised DRAE-NN) that uses dynamic information of the process along the time horizon. Based on this network a hierarchical structure is formulated by grouping faults based on their similarity into subsets of faults for detection and diagnosis. Further, an external pseudo-random binary signal (PRBS) is designed and injected into the system to identify incipient faults. The hierarchical structure based strategy improves the detection and classification accuracy significantly for both incipient and non-incipient faults. The proposed approach is tested on the benchmark Tennessee Eastman Process resulting in significant improvements in classification as compared to both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies., Comment: Under Review

Published
2020
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38. Why Electric Vehicles?

Author

Jahangir, Hamidreza, Golkar, Masoud Aliakbar, Ahmadian, Ali, Elkamel, Ali, Ahmadian, Ali, editor, Mohammadi-ivatloo, Behnam, editor, and Elkamel, Ali, editor

Published
2020
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50. Leveraging machine learning in porous media.

Author

Delpisheh, Mostafa, Ebrahimpour, Benyamin, Fattahi, Abolfazl, Siavashi, Majid, Mir, Hamed, Mashhadimoslem, Hossein, Abdol, Mohammad Ali, Ghorbani, Mina, Shokri, Javad, Niblett, Daniel, Khosravi, Khabat, Rahimi, Shayan, Alirahmi, Seyed Mojtaba, Yu, Haoshui, Elkamel, Ali, Niasar, Vahid, and Mamlouk, Mohamed

Abstract

The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML), has had a significant impact on engineering and the fundamental sciences, resulting in advances in various fields. The use of ML has significantly enhanced data processing and analysis, eliciting the development of new and improved technologies. Specifically, ML is projected to play an increasingly significant role in helping researchers better understand and predict the behavior of porous media. Furthermore, ML models will be able to make use of sizable datasets, such as subsurface data and experiments, to produce accurate predictions and simulations of porous media systems. This capability could help optimize the design of porous materials for specific applications and improve the effectiveness of industrial processes. To this end, this review paper attempts to provide an overview of the present status quo in this context, i.e., the interface of ML and porous media in six different applications, namely, heat exchanger and storage, energy storage and combustion, electrochemical devices, hydrocarbon reservoirs, carbon capture and sequestration, and groundwater, stressing the advances made in the application of ML to porous media and offering insights into the challenges and opportunities for future research. Each section also entails a supplementary database of the literature as a spreadsheet, which includes the details of ML models, datasets, key findings, etc., and mentions relevant available online datasets that can be used to train ML models. Future research trends include employing hybrid models by combining ML models with physics-based models of porous media to improve predictions concerning accuracy and interpretability. [ABSTRACT FROM AUTHOR]

Published
2024
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