Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features
In the era of Internet of Things (IoT), remaining useful life (RUL) prediction of turbofan engines is crucial. Various deep learning (DL) techniques proposed recently to predict RUL for such systems have remained silent on the effect of environmental changes on machine reliability. This paper aims (...
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| author | Subrata Rath Deepjyoti Saha Subhashis Chatterjee Ashis Kumar Chakraborty |
| author_facet | Subrata Rath Deepjyoti Saha Subhashis Chatterjee Ashis Kumar Chakraborty |
| author_sort | Subrata Rath |
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| description | In the era of Internet of Things (IoT), remaining useful life (RUL) prediction of turbofan engines is crucial. Various deep learning (DL) techniques proposed recently to predict RUL for such systems have remained silent on the effect of environmental changes on machine reliability. This paper aims (i) to identify the change point in RUL trends and patterns (ii) to select the most relevant features, and (iii) to predict RUL with the selected features and identified change points. A two-stage feature-selection algorithm was developed, followed by a change point identification mechanism, and finally, a Bidirectional Long Short-Term Memory (BiLSTM) model was designed to predict RUL. The study utilizes NASA’s C-MAPSS data set to check the performance of the proposed methodology. The findings affirm that the proposed method enhances the stability of DL models, resulting in a 27.8% improvement in RUL prediction compared to popular and cutting-edge DL models. |
| format | Article |
| id | doaj-art-e8e8146cc1b7456bbc16ea2be91fe9c7 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-e8e8146cc1b7456bbc16ea2be91fe9c72025-01-10T13:18:20ZengMDPI AGMathematics2227-73902024-12-0113113010.3390/math13010130Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum FeaturesSubrata Rath0Deepjyoti Saha1Subhashis Chatterjee2Ashis Kumar Chakraborty3Statistical Quality Control and Operations Research Unit, Indian Statistical Institute, Pune 411038, IndiaDepartment of Mathematics & Computing, Indian Institute of Technology (ISM), Dhanbad 826004, IndiaDepartment of Mathematics & Computing, Indian Institute of Technology (ISM), Dhanbad 826004, IndiaStatistical Quality Control and Operations Research Unit, Indian Statistical Institute, Kolkata 700108, IndiaIn the era of Internet of Things (IoT), remaining useful life (RUL) prediction of turbofan engines is crucial. Various deep learning (DL) techniques proposed recently to predict RUL for such systems have remained silent on the effect of environmental changes on machine reliability. This paper aims (i) to identify the change point in RUL trends and patterns (ii) to select the most relevant features, and (iii) to predict RUL with the selected features and identified change points. A two-stage feature-selection algorithm was developed, followed by a change point identification mechanism, and finally, a Bidirectional Long Short-Term Memory (BiLSTM) model was designed to predict RUL. The study utilizes NASA’s C-MAPSS data set to check the performance of the proposed methodology. The findings affirm that the proposed method enhances the stability of DL models, resulting in a 27.8% improvement in RUL prediction compared to popular and cutting-edge DL models.https://www.mdpi.com/2227-7390/13/1/130remaining useful life (RUL)Internet of Things (IoT)sensorsBidirectional Long Short-Term Memory (BiLSTM)feature engineeringchange point |
| spellingShingle | Subrata Rath Deepjyoti Saha Subhashis Chatterjee Ashis Kumar Chakraborty Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features Mathematics remaining useful life (RUL) Internet of Things (IoT) sensors Bidirectional Long Short-Term Memory (BiLSTM) feature engineering change point |
| title | Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features |
| title_full | Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features |
| title_fullStr | Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features |
| title_full_unstemmed | Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features |
| title_short | Remaining Useful Life Prediction of Turbofan Engine in Varied Operational Conditions Considering Change Point: A Novel Deep Learning Approach with Optimum Features |
| title_sort | remaining useful life prediction of turbofan engine in varied operational conditions considering change point a novel deep learning approach with optimum features |
| topic | remaining useful life (RUL) Internet of Things (IoT) sensors Bidirectional Long Short-Term Memory (BiLSTM) feature engineering change point |
| url | https://www.mdpi.com/2227-7390/13/1/130 |
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