The method for training dataset forming recorded by analog sensors to determine the helicopter turboshaft engines efficiency

IF 8.9 1区 工程技术 Q1 ENGINEERING, MECHANICAL
Denys Baranovskyi , Serhii Vladov , Maryna Bulakh , Valerii Sokurenko , Oleksandr Muzychuk , Victoria Vysotska
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引用次数: 0

Abstract

This article presents a novel approach to intelligent monitoring and control of complex dynamic systems, focusing specifically on helicopter turboshaft engines during flight. The developed method includes multi-channel signal processing, adaptive discretization and quantization, temporal feature extraction, and singular spectrum analysis. The key components are median, mean, and Hilbert filtering to eliminate noise, as well as adaptive quantization and clustering (hierarchical, DBSCAN, Gaussian mixtures) to ensure homogeneity and representativeness of samples. The method was verified using a neural network model, demonstrating a mean square error (MSE) of no more than 0.025 on training, validation, and test data. As a numerical experiment part, the TV3-117 engine compressor’s efficiency installed on the Mi-8MTV helicopter was calculated. The results showed a maximum MSE deviation from the reference value of no more than 0.862%, which confirms the developed method’s high accuracy. The article also proves the theorem on homogeneity and representativeness of data, according to which, if the training and test samples satisfy the homogeneity criteria (according to the Fisher-Pearson and Fisher-Snedecor statistical criteria) and representativeness (according to cluster analysis), they can be considered suitable for use in practical problems of modeling, classification, and forecasting. This theorem’s theoretical justification confirms the need for strict quality control of samples before training models. Scenarios with artificial introduction of errors into the data were simulated, which led to calculations and confirmed the instability and importance of ensuring homogeneity and representativeness of datasets. The developed method allows for a significant increase in the accuracy of predicting and diagnosing anomalies in the helicopter turboshaft engine’s operation, providing a reliable basis for intelligent monitoring and control in real operating conditions.
利用模拟传感器记录的训练数据集形成方法确定直升机涡轴发动机效率
本文提出了一种复杂动态系统智能监测与控制的新方法,特别是针对直升机涡轴发动机的飞行过程。该方法包括多通道信号处理、自适应离散化和量化、时域特征提取和奇异谱分析。关键成分是中值、均值和希尔伯特滤波,以消除噪声,以及自适应量化和聚类(分层、DBSCAN、高斯混合),以确保样本的均匀性和代表性。使用神经网络模型验证了该方法,在训练、验证和测试数据上显示均方误差(MSE)不超过0.025。作为数值实验部分,对安装在米- 8mtv直升机上的TV3-117发动机压气机效率进行了计算。结果表明,该方法与参考值的最大MSE偏差不大于0.82%,表明该方法具有较高的准确度。本文还证明了数据的同质性和代表性定理,根据该定理,如果训练样本和测试样本满足同质性标准(根据Fisher-Pearson和Fisher-Snedecor统计标准)和代表性标准(根据聚类分析),则可以认为它们适合用于建模、分类和预测的实际问题。该定理的理论证明证实了在训练模型之前需要对样本进行严格的质量控制。模拟了人为引入数据误差的场景,从而进行了计算,并证实了确保数据集均匀性和代表性的不稳定性和重要性。该方法显著提高了直升机涡轴发动机运行异常预测诊断的准确性,为实际运行工况下的智能监控提供了可靠依据。
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来源期刊
Mechanical Systems and Signal Processing
Mechanical Systems and Signal Processing 工程技术-工程:机械
CiteScore
14.80
自引率
13.10%
发文量
1183
审稿时长
5.4 months
期刊介绍: Journal Name: Mechanical Systems and Signal Processing (MSSP) Interdisciplinary Focus: Mechanical, Aerospace, and Civil Engineering Purpose:Reporting scientific advancements of the highest quality Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems
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