基于IFD和AE的高速铁路滚子轴承故障自动诊断技术
Q3 Engineering
引用次数: 0
摘要
导读:随着技术的发展和政策的支持，高铁的时空布局正在逐步扩大，确保高安全运行变得至关重要。 目的:分析高速列车机电系统关键部件实时相关故障诊断技术，提出一种基于遗传支持向量机的故障自动诊断新方法。 方法:本研究将IFD和AE两种技术相结合，引入自适应加权算法对两者数据进行融合，并通过实验验证其准确性。 结果:实验结果表明，在IFD实验中，1050转速下的2点频率为347.6 Hz, 3点频率为498.4 Hz，两者都非常接近1点频率的2倍和3倍频率，乘法关系更加直接。 结论:IFD与AE相结合可以实现轴承状态的自动准确诊断和自适应加权融合算法对轴承的预诊断，在高速铁路滚动轴承故障的实际机械诊断中是有效的。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Automatic Fault Diagnosis Technology of Roller Bearings of High-speed Rail Based on IFD and AE
INTRODUCTION: With the development of technology and policy support, high-speed rail's temporal and spatial layout is gradually expanding, and it becomes essential to ensure high-safety operation.
OBJECTIVES: The real-time correlation fault diagnosis technology of critical components of electromechanical systems of high-speed trains is analyzed, and a new method of automatic fault diagnosis based on genetic support vector machine is proposed.
METHODS: In this study, the Author combines two techniques, IFD and AE, and introduces an adaptive weighting algorithm to fuse the data of the two and experimentally verify their accuracy.
RESULTS: The experimental results show that in the IFD experiment, the 2-point frequency at 1050 speed is 347.6 Hz, and the 3-point frequency is 498.4 Hz, both of which are very close to the 2 and 3 times frequencies of the 1-point frequency, and the multiplicative relationship is much more straightforward.
CONCLUSION: Combining IFD and AE can realize automatic and accurate diagnosis of bearing state and pre-diagnosis of bearings by adaptive weighted fusion algorithm, which is effective in the practical mechanical diagnosis of rolling bearing faults in high-speed railroads.
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来源期刊
EAI Endorsed Transactions on Energy Web
Energy-Energy Engineering and Power Technology
CiteScore
2.60
自引率
0.00%
发文量
14
审稿时长
10 weeks
期刊介绍：
With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.
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