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      •   BUIR Home
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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Early diagnosis of breakdown through transfer learning

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      Author
      Özbek, Seren
      Advisor
      Güvenir, H. Altay
      Date
      2019-06
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      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 learning
      Predictive maintenance
      Fault diagnosis
      Deep learning
      LSTM
      Canonical correlation analysis
      Embargo Lift Date
      2019-12-14
      Permalink
      http://hdl.handle.net/11693/52053
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      • Dept. of Computer Engineering - Master's degree 489

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