An Empirical Knowledge Representation Model Based on Similarity Measures of Multi-compression-Layers for Lre Operational State Identification

Fudong Li, Zijun Liu, Jinglong Chen
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Abstract

Data-driven intelligent fault diagnosis methods for liquid rocket engine (LRE) operational condition identification have good prospects for application. However, the small number of LRE samples and the highly unbalanced categories pose difficulties for the training of intelligent diagnostic models. To address the problem of the unbalanced distribution of LRE sample categories, an empirical knowledge reconstruction expression model is proposed. Instead of the traditional sample-label one-to-one training model, the model represents the operational status of the input samples by measuring the similarity between the input samples and each sample in the historical knowledge base, combined with the expert knowledge in the historical experience base. The model combines the powerful data mining capability of the neural network model with the precise point analysis capability of the expert knowledge, while the continuously improved historical sample distribution and expert knowledge and experience base provide the scientific basis for the state discrimination and improvement direction of LRE. Further, the validity and engineering value of the proposed method was verified using real LRE test drive data.
基于多压缩层相似性测度的Lre运行状态识别经验知识表示模型
数据驱动的智能故障诊断方法在液体火箭发动机运行状态识别中具有良好的应用前景。然而,LRE样本数量少,分类高度不平衡,给智能诊断模型的训练带来了困难。针对LRE样本类别分布不平衡的问题,提出了一种经验知识重构表达模型。该模型取代了传统的样本-标签一对一训练模型,通过测量输入样本与历史知识库中每个样本的相似度,并结合历史经验库中的专家知识来表示输入样本的运行状态。该模型将神经网络模型强大的数据挖掘能力与专家知识的精确点分析能力相结合,不断完善的历史样本分布和专家知识经验库为LRE的状态判别和改进方向提供了科学依据。最后,通过实际LRE测试数据验证了该方法的有效性和工程价值。
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