LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines
Working machinery has a limited lifespan and occasionally breaks down due to obsolete functioning. A maintenance strategy must be used on the scheduled machinery system in order to prevent the worst scenario (failure) and learn more about the machine’s condition. The ideal maintenance technique is p...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_03004.pdf |
| _version_ | 1797343982345256960 |
|---|---|
| author | Upadhyay Rupali Amhia Hemant |
| author_facet | Upadhyay Rupali Amhia Hemant |
| author_sort | Upadhyay Rupali |
| collection | DOAJ |
| description | Working machinery has a limited lifespan and occasionally breaks down due to obsolete functioning. A maintenance strategy must be used on the scheduled machinery system in order to prevent the worst scenario (failure) and learn more about the machine’s condition. The ideal maintenance technique is predictive maintenance. Predictive maintenance, whose major goal is to forecast hardware component failures by continually monitoring their status, emphasizes the importance of this requirement so that maintenance operations can be planned far in advance. These observations, which span the lifecycle of the corresponding components, are produced by monitoring systems and often take the form of time series and event logs. The fundamental difficulty of data-driven predictive maintenance is analysing this history of observations in order to create prediction models. Machine learning has become widely used in this direction as a result of its ability to extract knowledge from a range of data sources with the least amount of human involvement. This work aims to investigate and deal with difficult issues in aviation connected to foreseeing component breakdowns on board. Scalability is a crucial component of every suggested strategy due to the vast volume of data related to the operation of airplanes. |
| first_indexed | 2024-03-08T10:55:43Z |
| format | Article |
| id | doaj.art-e4593a61676d4fffba2b88d3fd1ac2c0 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T10:55:43Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-e4593a61676d4fffba2b88d3fd1ac2c02024-01-26T16:34:28ZengEDP SciencesITM Web of Conferences2271-20972023-01-01570300410.1051/itmconf/20235703004itmconf_icaect2023_03004LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft EnginesUpadhyay Rupali0Amhia Hemant1EE Department, Jabalpur Engineering CollegeEE Department, Jabalpur Engineering CollegeWorking machinery has a limited lifespan and occasionally breaks down due to obsolete functioning. A maintenance strategy must be used on the scheduled machinery system in order to prevent the worst scenario (failure) and learn more about the machine’s condition. The ideal maintenance technique is predictive maintenance. Predictive maintenance, whose major goal is to forecast hardware component failures by continually monitoring their status, emphasizes the importance of this requirement so that maintenance operations can be planned far in advance. These observations, which span the lifecycle of the corresponding components, are produced by monitoring systems and often take the form of time series and event logs. The fundamental difficulty of data-driven predictive maintenance is analysing this history of observations in order to create prediction models. Machine learning has become widely used in this direction as a result of its ability to extract knowledge from a range of data sources with the least amount of human involvement. This work aims to investigate and deal with difficult issues in aviation connected to foreseeing component breakdowns on board. Scalability is a crucial component of every suggested strategy due to the vast volume of data related to the operation of airplanes.https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_03004.pdf |
| spellingShingle | Upadhyay Rupali Amhia Hemant LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines ITM Web of Conferences |
| title | LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines |
| title_full | LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines |
| title_fullStr | LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines |
| title_full_unstemmed | LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines |
| title_short | LSTM-Based Approach for Remaining Useful Life Prediction of Air Craft Engines |
| title_sort | lstm based approach for remaining useful life prediction of air craft engines |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_03004.pdf |
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