Glen, Chosua and Schwung, Andreas and Rusyadi, Rusman (2019) Conditional Monitoring of Industrial Processes With Deep Neural Networks. Bachelor thesis, Swiss German University.

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Abstract

In this paper a condition monitoring an in industrial processes is made with deep learning algorithm, with data served from IEEE PHM Challenge of the bearings‟ degrading life. To provide the best many-to-many type of learning process, sequential type of deep learning is chosen such as Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Encoder-Decoder Model, and Sequence to Sequence Attention Model will be used to make Remaining Useful Life (RUL) prediction based on the actual RUL of the tested bearings with end-to-end approach. Comparison and discussion will be written in detail.

Item Type:	Thesis (Bachelor)
Uncontrolled Keywords:	Deep Learning ; Process Monitoring ; Sequential ; Regression ; Many to Many
Subjects:	T Technology > TJ Mechanical engineering and machinery
Divisions:	Faculty of Engineering and Information Technology > Department of Mechatronics Engineering
Depositing User:	Adityatama Ratangga
Date Deposited:	19 May 2020 14:01
Last Modified:	19 May 2020 14:01
URI:	http://repository.sgu.ac.id/id/eprint/642

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