基于翼上传感数据的退化增强APU性能评估

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xiaolei Liu;Liansheng Liu;Lulu Wang;Xiyuan Peng;Datong Liu
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引用次数: 0

摘要

飞机辅助动力装置(APU)是一种小型涡轮发动机,为飞机提供动力和气源。它的主要作用是帮助启动主发动机并为飞机提供电力。准确的翼上辅助动力装置性能评估有助于提高辅助动力装置的安全性,同时减少航空公司不必要的维护成本。由于恶劣的工作环境和工作条件,其性能参数受到很大影响。对翼上APU进行PA是一个难点。本文提出了一种基于退化特征增强的多参数PA方法来实现翼上APU的PA。首先,提出了一种自适应特征提取变分模态分解方法,从翼上传感数据中提取退化特征,得到监测参数的特征集;然后,通过长短期记忆(LSTM)网络对提取的退化特征进行融合,实现特征识别。为了评估该方法的有效性,基于航空公司的真实翼上传感数据进行了实验。仿真结果表明,该方法可以获得较好的仿真结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Assessment of APU Based on Degradation Enhancement With On-Wing Sensing Data
The aircraft auxiliary power unit (APU) is a small turbine engine that provides power and air sources for the aircraft. Its main role is to help start the main engine and provide electric power to the aircraft. The accurate performance assessment (PA) of on-wing APUs can help improve the safety of APUs while reducing unnecessary maintenance costs for airlines. Due to the hostile operating environment and working conditions, the performance parameters are affected greatly. It is difficult to conduct the PA for on-wing APU. In this article, a multiparameter PA approach based on degradation feature enhancement is proposed to fulfill the PA of on-wing APU. First, an adaptive feature extraction variational mode decomposition is proposed to extract the degradation features from the on-wing sensing data and obtain a feature set of the monitored parameters. Then, the extracted degradation features are fused through a long short-term memory (LSTM) network for achieving PA. To evaluate the effectiveness of the proposed method, experiments are conducted based on real on-wing sensing data from airlines. The PA results show that the proposed approach can obtain better PA results.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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