电子测量与仪器学报

张晓瑜,邓佐青,唐黎伟,郭润夏,吴军.基于 DaLSTM 组合模型的电动舵机故障诊断方法[J].电子测量与仪器学报,2022,36(11):70-78

基于 DaLSTM 组合模型的电动舵机故障诊断方法

Fault diagnosis method of electro-mechanical actuatorsbased on DaLSTM combined model

DOI：

英文关键词:electro-mechanical actuators fault diagnosis combined model LSTM

基金项目:国家自然科学基金（62173331,52005500）、天津市教委科研计划项目（2020KJ013）资助

作者	单位
张晓瑜	1. 中国民航大学电子信息与自动化学院
邓佐青	1. 中国民航大学电子信息与自动化学院
唐黎伟	1. 中国民航大学电子信息与自动化学院
郭润夏	1. 中国民航大学电子信息与自动化学院
吴军	2. 中国民航大学航空工程学院

Author	Institution
Zhang Xiaoyu	1. College of Electronic Information and Automation, Civil Aviation University of China
Deng Zuoqing	1. College of Electronic Information and Automation, Civil Aviation University of China
Tang Liwei	1. College of Electronic Information and Automation, Civil Aviation University of China
Guo Runxia	1. College of Electronic Information and Automation, Civil Aviation University of China
Wu Jun	2. College of Aeronautical Engineering, Civil Aviation University of China

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中文摘要:

为了实现电动舵机工作过程中多种故障的一体化诊断,提出了一种基于双阶段注意力的长短期记忆网络(DaLSTM)组合模型的故障诊断方法。首先,将电动舵机的多源传感器信号作为输入,采用基于输入注意力和时间注意力的长短期记忆网络 (LSTM)自适应提取原始多源传感器数据中的相关特征,并通过 DaLSTM 组合模型实现多源传感器的时间序列预测。其次,在故障诊断时间窗口内,以不同工作状态下 DaLSTM 组合模型预测值与采样值的差值最小为决策函数诊断电动舵机的故障类型。最后,利用公开的美国国家航空航天局(National Aeronautics and Space Administration, NASA)数据集进行时间序列预测和故障诊断实验,对故障类别的平均识别率达到了 98. 76%,证明了该方法的有效性。

英文摘要:

In order to realize the integrated diagnosis of multiple faults in the working process of electro-mechanical actuators (EMA), a fault diagnosis method of EMA based on dual-stage attention-based long short term memory (DaLSTM) combined model was proposed. Firstly, the multi-source sensor signal of the EMA is used as the input. The long short term memory (LSTM) neural network based on input attention and time attention is used to adaptively extract the relevant features in the original multi-source sensor data, and the time series prediction of multi-source sensors is realized by the DaLSTM combination model. Secondly, in the fault diagnosis time window, the minimum difference between the predicted value and the sampled value of the DaLSTM combination model under different states is used as the decision function to diagnose the fault type of EMA. Finally, time series prediction and fault diagnosis experiments are conducted using the public National Aeronautics and Space Administration (NASA) dataset, and the average recognition rate of fault categories reaches 98. 76%, which proves the effectiveness of the proposed method.

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