{"title":"Measurement and analysis of dynamic load sharing in planetary gear trains considering high power operations and system errors","authors":"Cheng Wang","doi":"10.1016/j.measurement.2025.117828","DOIUrl":null,"url":null,"abstract":"<div><div>Studying load sharing in planetary gear trains (PGTs) is crucial for enhancing their load capacity and minimizing vibration and noise. One of the primary factors influencing load sharing is the presence of various types of errors within the system. PGTs are inherently complex mechanical systems that involve numerous types of errors, making it impossible for theoretical models to comprehensively capture all of these errors. Therefore, research often necessitates focusing on certain errors, inevitably leading to limited outcomes. Current research has also emphasized this point. Experimentation is the most effective methodology; however, current experiments have not fully accounted for high-power operating conditions due to experimental costs and methodologies. High power is another important factor affecting load sharing. The load sharing coefficient is used to quantify the degree of load distribution among its components. In response to the aforementioned challenges, this paper proposes a precise measurement methodology for the dynamic load sharing coefficient of PGTs under high-power conditions, taking into account integrated errors within the system. Based on the processing and analysis of a large amount of collected data, the influence of high power (including rotational speed and torque) and the number of planetary gears on the dynamic load sharing coefficient is investigated. This work provides guidance for improving the load-bearing capacity and reducing vibration and noise in PGTs.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117828"},"PeriodicalIF":5.2000,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S026322412501187X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Studying load sharing in planetary gear trains (PGTs) is crucial for enhancing their load capacity and minimizing vibration and noise. One of the primary factors influencing load sharing is the presence of various types of errors within the system. PGTs are inherently complex mechanical systems that involve numerous types of errors, making it impossible for theoretical models to comprehensively capture all of these errors. Therefore, research often necessitates focusing on certain errors, inevitably leading to limited outcomes. Current research has also emphasized this point. Experimentation is the most effective methodology; however, current experiments have not fully accounted for high-power operating conditions due to experimental costs and methodologies. High power is another important factor affecting load sharing. The load sharing coefficient is used to quantify the degree of load distribution among its components. In response to the aforementioned challenges, this paper proposes a precise measurement methodology for the dynamic load sharing coefficient of PGTs under high-power conditions, taking into account integrated errors within the system. Based on the processing and analysis of a large amount of collected data, the influence of high power (including rotational speed and torque) and the number of planetary gears on the dynamic load sharing coefficient is investigated. This work provides guidance for improving the load-bearing capacity and reducing vibration and noise in PGTs.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.