The invention discloses a fault diagnosis method based on a long and short term memory neural network, and the method comprises the following steps: 1, collecting the related information of aviation equipment, and carrying out the data preprocessing, the data preprocessing content comprising data collection, data cleaning and data normalization; 2, feature extraction: performing feature extraction on the preprocessed data; 3, model construction: constructing an LSTM model according to the extracted features; 4, model training: training the constructed LSTM model; step 5, fault diagnosis is carried out, model testing is carried out, and a model testing result is obtained; and step 6, carrying out model optimization, optimizing the LSTM model according to a test result and an actual demand, and deploying the optimized LSTM model to an actual system for fault diagnosis. According to the invention, the fault diagnosis of the aviation equipment can be more accurate and rapid.
本发明公开了一种基于长短时记忆神经网络的故障诊断方法,包括以下步骤:步骤一:采集航空设备相关信息并进行数据预处理,数据预处理内容包括数据预处理内容包括数据收集、数据清洗与数据归一化;步骤二:特征提取,对预处理完成的数据进行特征提取;步骤三:模型构建,根据提取的特征,构建LSTM模型;步骤四:模型训练,对构建的LSTM模型进行训练;步骤五:故障诊断,进行模型测试,获取到模型测试结果;步骤六:进行模型优化,根据测试结果和实际需求,对LSTM模型进行优化,将优化后的LSTM模型部署到实际系统中进行故障诊断。本发明能够更加准确快速的航空设备的故障诊断。
Fault diagnosis method based on long and short term memory neural network
一种基于长短时记忆神经网络的故障诊断方法
25.04.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / B64F GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT , Boden- oder Flugzeugträgerdeckeinrichtungen besonders ausgebildet für die Verwendung in Verbindung mit Luftfahrzeugen / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung |
Research on Fault Diagnosis Algorithm Based on Bi-directional Long Short-Term Memory
Springer Verlag | 2021
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