An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.
|Titel||IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society|
|Publikationsdato||18 okt. 2020|
|Status||Udgivet - 18 okt. 2020|
|Begivenhed||46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore|
Varighed: 18 okt. 2020 → 21 okt. 2020
|Konference||46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020|
|Periode||18/10/2020 → 21/10/2020|
|Sponsor||IEEE Industrial Electronics Society (IES), SPECS - Smart Grid + Power Electronics Consortium Singapore, The Institute of Electrical and Electronics Engineers (IEEE)|
|Navn||Proceedings of the Annual Conference of the IEEE Industrial Electronics Society|
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© 2020 IEEE.