Abnormal performance detection method of civil aircraft air conditioning heat exchanger based on LSTM-AE
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NETL
NSTL
万方数据
空调热交换器性能异常检测技术是快速判断民机空调系统运行状态并合理安排维修任务的关键,传统的异常检测方法难以有效处理高维时序数据,无法实现系统早期故障预警.为此,本文提出了一种基于长短期记忆网络(LSTM,long-short term memory)与自编码器(AE,autoencoder)模型的无监督异常检测方法,用以识别民机空调系统异常运行状态.首先,基于民机空调系统原始传感器参数构建表征空调热交换器性能的特征监测参数;其次,构建LSTM-AE 模型进行数据特征重构并计算重构误差;最后,使用孤立森林(iForest,isolation forest)进行无监督异常监测.将本文构建的无监督异常检测方法与传统方法对比,并建立模型评估指标,验证结果表明,所构建的模型方法可以对民机空调热交换器性能异常状态进行有效检测.
The key to quickly judging the operating state of the air conditioning system and efficiently arranging maintenance tasks lies in the abnormal performance detection technology of the civil aircraft air conditioning heat exchanger.However,traditional abnormal detection methods struggle to effectively handle high-dimensional time series data and fail to provide early fault warning for the system.In light of this,this paper proposes an unsupervised anomaly detection method based on long-short term memory(LSTM)and autoencoder(AE)to identify abnormal operating state of the civil aircraft air conditioning system.Firstly,characteristic monitoring parameters that reflect the per-formance of the air conditioning heat exchanger is constructed based on the original sensor parameters of the civil aircraft air conditioning system.Subsequently,the LSTM-AE model is employed to reconstruct the data features and calculate the reconstruction errors.Finally,unsupervised anomaly monitoring is conducted using isolation for-est(iForest).The constructed unsupervised anomaly detection method is compared with the traditional method,and a model is established to evaluate indicator.The verification results demonstrate that the proposed model can effec-tively detect abnormal states of the performance of civil aircraft air conditioning heat exchanger.
civil aircraft air conditioning systemabnormal detectionautoencoder(AE)long-short term memory(LSTM)isolation forest(iForest)
NETL
NSTL
万方数据
