Early diagnosis of breakdown through transfer learning
Author
Özbek, Seren
Advisor
Güvenir, H. Altay
Date
2019-06Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Show full item recordAbstract
Breakdown prediction of equipment is an essential task considering the management
of resources and maintenance operations. Early diagnosis systems allow
creating alerts on time for taking precautions on production. A significant challenge
for diagnosis is to have an insufficient size of data, yet, transfer learning
approaches can alleviate such an issue when there is a constrained supply of training
data. We intend to improve the reliability of breakdown prediction when
there is a limited quantity of training data. We recommend similarity correlation
on Remaining Useful Life of these equipment. To do this, we offer learning a
common feature space between the target and the source equipment, where we
acquire prior knowledge from the source that has different measurements than
the target. Within the learned joint feature matrices, we train our model on the
vast amount of data of different equipment and finetune it using the data of our
target equipment. In this way, we aim to obtain an accurate and reliable model
for early breakdown prediction.
Keywords
Transfer learningPredictive maintenance
Fault diagnosis
Deep learning
LSTM
Canonical correlation analysis