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  <doi_batch_id>aspg-39-2411-1792223089</doi_batch_id>
  <timestamp>20261017074449</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Neutrosophic and Information Fusion</full_title>
    <abbrev_title>NIF</abbrev_title>
    <issn media_type="electronic">2836-7863</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>2</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Multi-Dimensional Sensor Fusion for Proactive Maintenance in Pump Systems</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Samandarboy</given_name>
      <surname>Sulaymanov</surname>
      <affiliations>
       <institution>
        <institution_name>Department of International Business Management, Tashkent State University of Economics, Uzbekistan</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This study focuses on the task of maintenance, in pump systems by utilizing a combination of multi dimensional sensor fusion and advanced machine learning techniques. Pump systems play a role in settings but unexpected failures can lead to significant disruptions and operational inefficiencies. The goal of this research is to predict and prevent these failures effectively. To achieve this we analyzed a dataset consisting of 52 sensor units and over 220,000 readings. By applying Principal Component Analysis (PCA) we were able to extract information and reduce complexity gaining an understanding of how the pump system behaves. We then utilized Long Short Term Memory (LSTM) networks to learn from the combined sensor data enabling predictions and early detection of faults that're vital for proactive maintenance strategies. Our findings demonstrate the potential of these methodologies. The integration of sensor data sources and the use of PCA for dimensionality reduction allowed us to obtain a view while LSTM networks effectively captured the temporal dynamics present, in the sensor data leading to precise predictions regarding system behavior.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>15</first_page>
     <last_page>23</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2411</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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    <doi_data>
     <doi>10.54216/NIF.020202</doi>
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