提高机电系统诊断故障分类模型的质量

M. Nadezhin, N. Slobodzyan, A. Kiselev
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引用次数: 0

摘要

该研究是在俄罗斯联邦科学和高等教育部的财政支持下,以乌斯提诺夫命名的BSTU“VOENMEH”目前开展的工作的一部分,旨在设计和创造用于航空、运输和空间技术的高资源电动抽油机。介绍了星载机电单元技术状态诊断系统的硬件支持和算法支持。在电泵单元实验室样品的实验研究过程中,对所提出的解决方案进行了地面测试。混合特征选择算法在显著减少输入值数量的情况下,提高了前馈人工神经网络诊断的准确性和速度。已经确定了对机电系统电气部分状态变化敏感的量。关键词诊断,电机,机器学习,特征选择,分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving quality of the faults’ classification models for electromechanical systems’ diagnostics
The research is part of the current work carried out at BSTU «VOENMEH» named after D.F. Ustinov with the finan-cial support of the Ministry of Science and Higher Education of the Russian Federation for the design and creation of high-resource electric pumping units for aviation, transport, and space technology. Recommendations are given on the hardware and algorithmic support of the on-board system for diagnosing the technical condition of spacecraft electromechanical units. Ground testing of the proposed solutions was carried out in the course of experimental studies of an electric pump unit laboratory sample. The advantages of the hybrid feature selection algorithm for improving the accuracy and speed of diagnostics with a feedforward artificial neural network with a significant decrease in the number of input values are shown. The quantities that are sensitive to changes in the state of the electrical parts of electromechanical systems have been determined. Key words Diagnostics, electric motor, machine learning, feature selection, classification.
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