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.

Jiantao Lu, Kuangzhi Yang, Peng Zhang, Wei Wu, Shunming Li

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Jiantao LuCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, China.ORCID 0000-0002-9674-165X

Kuangzhi YangCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, China.

Peng ZhangAECC Guiyang Engine Design Research Institute, Guiyang 550081, China.

Wei WuCollege of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210001, China.

Shunming LiSchool of Automotive Engineering, Nantong Institute of Technology, Nantong 226002, China.

Funding

Jiangsu Provincial Double-Innovation Doctor Program 202030364National Natural Science Foundation of China 52405113Scientific Research Foundation for NUAA YAH20008Youth Fund project of Natural Science Foundation of Jiangsu Province BK20241401

6 · The paper itself

Abstract

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

Indexed as

enhanced SANL1 filteringLSTMmulti-operating conditiontrend forecasting

Identifiers

PMID40218581

DOI10.3390/s25072066

PMCPMC11991262

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.