A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions
Trend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (LSTM) neural network under multi-operating conditio...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2066 |
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| author | Jiantao Lu Kuangzhi Yang Peng Zhang Wei Wu Shunming Li |
| author_facet | Jiantao Lu Kuangzhi Yang Peng Zhang Wei Wu Shunming Li |
| author_sort | Jiantao Lu |
| collection | DOAJ |
| description | Trend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (LSTM) neural network under multi-operating conditions. Firstly, a condition recognition technology is constructed to automatically identify the operating conditions based on the predetermined judgment conditions, and vibration signal features are adaptively divided into three typical operating conditions, namely, the idling operating condition, the starting operating condition and the utmost operating condition. The features of original signals are extracted to reduce the impacts of signal fluctuations and noise preliminarily. Secondly, enhanced SAN is used to normalize and denormalize the features to alleviate non-stationary factors. To improve prediction accuracy, an <i>L</i><sub>1</sub> filter is adopted to extract the trend term of the features, which can effectively reduce the overfitting of SAN to local information. Moreover, the slice length is quantitatively estimated by the fixed points in <i>L</i><sub>1</sub> filtering, and a tail amendment technology is added to expand the applicable range of enhanced SAN. Finally, an LSTM-based forecasting model is constructed to forecast the normalized data from enhanced SAN, serving as input during denormalization. The final results under different operating conditions are the output from denormalization. The validity of the proposed method is verified using the test data of an aircraft engine. The results show that the proposed method can achieve higher forecasting accuracy compared to other methods. |
| format | Article |
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| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-bfdfc29d52744879a2c521b4e019764f2025-08-20T02:15:46ZengMDPI AGSensors1424-82202025-03-01257206610.3390/s25072066A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating ConditionsJiantao Lu0Kuangzhi Yang1Peng Zhang2Wei Wu3Shunming Li4College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, ChinaCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, ChinaAECC Guiyang Engine Design Research Institute, Guiyang 550081, ChinaCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, ChinaSchool of Automotive Engineering, Nantong Institute of Technology, Nantong 226002, ChinaTrend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (LSTM) neural network under multi-operating conditions. Firstly, a condition recognition technology is constructed to automatically identify the operating conditions based on the predetermined judgment conditions, and vibration signal features are adaptively divided into three typical operating conditions, namely, the idling operating condition, the starting operating condition and the utmost operating condition. The features of original signals are extracted to reduce the impacts of signal fluctuations and noise preliminarily. Secondly, enhanced SAN is used to normalize and denormalize the features to alleviate non-stationary factors. To improve prediction accuracy, an <i>L</i><sub>1</sub> filter is adopted to extract the trend term of the features, which can effectively reduce the overfitting of SAN to local information. Moreover, the slice length is quantitatively estimated by the fixed points in <i>L</i><sub>1</sub> filtering, and a tail amendment technology is added to expand the applicable range of enhanced SAN. Finally, an LSTM-based forecasting model is constructed to forecast the normalized data from enhanced SAN, serving as input during denormalization. The final results under different operating conditions are the output from denormalization. The validity of the proposed method is verified using the test data of an aircraft engine. The results show that the proposed method can achieve higher forecasting accuracy compared to other methods.https://www.mdpi.com/1424-8220/25/7/2066trend forecasting<i>L</i><sub>1</sub> filteringenhanced SANLSTMmulti-operating condition |
| spellingShingle | Jiantao Lu Kuangzhi Yang Peng Zhang Wei Wu Shunming Li A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions Sensors trend forecasting <i>L</i><sub>1</sub> filtering enhanced SAN LSTM multi-operating condition |
| title | A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions |
| title_full | A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions |
| title_fullStr | A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions |
| title_full_unstemmed | A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions |
| title_short | A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions |
| title_sort | trend forecasting method for the vibration signals of aircraft engines combining enhanced slice level adaptive normalization using long short term memory under multi operating conditions |
| topic | trend forecasting <i>L</i><sub>1</sub> filtering enhanced SAN LSTM multi-operating condition |
| url | https://www.mdpi.com/1424-8220/25/7/2066 |
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