基于CAE-LSTM的航发轴承故障诊断方法

Fault Diagnosis Method for Aero-engine Bearings Based on CAE-LSTM

摘要

随着计算机深度学习理论的发展及航空领域对关键装备智能化故障诊断与运维需求的提升,基于深度学习的航空发动机运行状态的监测与评估方法成为了飞机安全运行的重要保障.由于航空发动机机械结构复杂,轴承在高温、高压等恶劣环境下高速运行,其故障特征信息存在多尺度、非线性等问题,使得故障信号难以有效识别及分析诊断.因此,本文提出了一种基于CAE-LSTM的航发轴承故障诊断方法,首先利用改进的卷积自编码器(Convolutional Autoencoder,CAE)对高维振动信号进行降维和特征提取,然后将提取到的特征输入到长短期记忆网络(Long Short-Term Memory,LSTM)分类器中进行故障类型识别,从而提升轴承故障分类的准确性和鲁棒性.实验结果表明本文提出的方法能够有效地学习航发轴承传感信号序列中的动态特征,提高航发轴承故障诊断的精确性和智能性.

Abstract

With the development of computer deep learning theory and the enhancement of the demand for intelligent fault diagnosis and operation and maintenance of key equipment in the field of aviation,the monitoring and evaluation method of the operating state of aero-engine based on deep learning has become an important guarantee for the safe operation of aircraft.Due to the complexity of the mechanical structure of the aero-engine and the high-speed operation of the bearings in a harsh environment such as high temperature and high pressure,there are problems such as multi-scale and non-linearity in the fault characteristic information,which makes it difficult to effectively identify and analyze and diagnose the fault signals.Therefore,this paper proposes a CAE-LSTM-based fault diagnosis method for airframe bearings,which firstly utilizes the improved Convolutional Autoencoder (CAE) to perform downscaling and feature extraction for high-dimensional vibration signals,and then inputs the extracted features into the Long Short-Term Memory (LSTM) classification network.Memory (LSTM) classifier for fault type identification,thus improving the accuracy and robustness of bearing fault classification.The experimental results show that the method proposed in this paper can effectively learn the dynamic features in the aircraft bearing sensing signal sequence and improve the accuracy and intelligence of aircraft bearing fault diagnosis.

关键词

航空发动机 / 卷积自编码器 / 长短期记忆网络 / 特征提取 / 故障诊断

Key words

aero-engine / convolutional autoencoder / long short-term memory / feature extraction / fault diagnosis

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基金资助

辽宁省科技重大专项项目(2024JH1/1170043)资助.