Influence of the Carrier Pinhole Position Errors on the Load Sharing of a Planetary Gear Train

Kim, Jeong-Gil,Park, Young-Jun,Lee, Sang-Dae,Oh, Joo-young,Kim, Jae-Hoon,Lee, Geun-Ho Korean Society for Precision Engineering 2018 International Journal of Precision Engineering and Vol.19 No.4

Load sharing among planetary gears, one of the design variables, has a significant influence on the performance and service life of a gearbox. This study involved simulating and testing the design parameters related to load sharing among planetary gears. In this regard, the influence of errors in the carrier pinhole position on the load sharing among the planetary gears was analyzed. The results showed that the difference between the simulation results using the model and the laboratory test results was less than 10%. Furthermore, similar tendencies were observed according to the magnitude of the load applied to the planetary gears. As for the design parameters affecting load sharing, the service life of a gearbox containing planetary gears can be extended by using a floating system as opposed to a non-floating system. In addition, reduced planetary pin diameter and increased planetary bearing clearance leads to appropriate load sharing among the planetary gears and increases the service life and floating effect of the gearbox.

New degradation feature extraction method of planetary gearbox based on alpha stable distribution

Wenxin Qiao,Xianglong Ni,Lei Wang,Xin Lv,Liwei Chen,Fucheng Sun 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.1

The planetary transmission system has been widely used in industry because of its various advantages. And the study on degradation feature extraction method of planetary gearbox is of major significance for mechanical system prognostics and health management (PHM). In this paper, the alpha stable distribution characteristics of planetary gearbox vibration signals in performance degradation process are verified. By observing the change of alpha stable distribution for planetary gearbox degradation experiment data, a new degradation feature extraction method based on alpha stable distribution is proposed, which is called the height of probability distribution (HPD). Through comparative analysis, it is determined that HPD has better linearity and less fluctuation compared with conventional degradation features in planetary gearbox accelerated degradation stage. Moreover, in the accelerated degradation stage, the degradation trend prediction result based on HPD is closer to the actual data than conventional degradation features no matter using Wiener-based or LSSVM-based prediction method. These conclusions indicate that the newly proposed HPD works well and gives accurate estimates for condition monitoring and degradation trend prediction of planetary gearbox.

Fault diagnosis of planetary gearbox with incomplete information using assignment reduction and flexible naive Bayesian classifier

Jun Yu,Mingyou Bai,Guannan Wang,Xianjiang Shi 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.1

In planetary gearbox operation, there are many uncertain factors that may result in incomplete diagnostic information, such as measurement instrument faults, limitation of transmission capacity, and data processing. Therefore, it has been one of the greatest obstacles to fault diagnosis of planetary gearbox. To address this issue, a novel fault diagnosis method of planetary gearbox with incomplete information using assignment reduction and Flexible naive Bayesian classifier (FNBC) is proposed. Characteristic relation was utilized to preprocess incomplete diagnostic information. Then, assignment reduction algorithm based on characteristic relation was used to remove irrelevant or redundant condition attribute values. Finally, FNBC was constructed to reason diagnosis results. To validate the performance of the proposed method, a fault diagnosis experiment was conducted. The experimental studies demonstrate the proposed method can be utilized to diagnose planetary gearbox faults with incomplete diagnostic information, reduce computational complexity, and enhance reasoning accuracy.

Design and evaluation of two-stage planetary gearbox for special-purpose industrial machinery

Zhen Qin,Yu-Ting Wu,Amre Eizad,이기훈,류성기 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.12

The gearbox, as a traditional machine that changes the transmission ratio and transfers power safely, stably, quietly and efficiently, is an irreplaceable component in the field of machinery design. The planetary gear train is the most widely used of the traditional gearbox designs. Although scholars have carried out a variety of research on gearboxes, gear failures, noise and other issues are still common. This article presents a study involving the initial macro geometry design, gear flank modification, static and dynamic analysis, and experimental verification of a two-stage planetary gearbox for special-purpose industrial machinery. Compared to traditional static transmission error simulation, this study presents a novel analysis of the gearbox overall dynamic transmission error. In this research, the modal flexibility is also analyzed to determine the possibility of resonance in the gearbox. As a result, the strength of this gear system is guaranteed by macro design. PPTE (noise evaluation index) of each gear pair is greatly improved after flank modification. The efficiency of power transmission system is also improved with the improvement of vibration. The result of final bench test of the prototype is also quite satisfactory. This also verified the correctness of the theoretical simulation method presented in this research.

Robust deep learning-based fault detection of planetary gearbox using enhanced health data map under domain shift problem

Hwang Taewan,하종문,윤병동 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4

The conventional deep learning-based fault diagnosis approach faces challenges under the domain shift problem, where the model encounters different working conditions from the ones it was trained on. This challenge is particularly pronounced in the diagnosis of planetary gearboxes due to the complicated vibrations they generate, which can vary significantly based on the system characteristics of the gearbox. To solve this challenge, this paper proposes a robust deep learning-based fault-detection approach for planetary gearboxes by utilizing an enhanced health data map (HDMap). Although there is an HDMap method that visually expresses the vibration signal of the planetary gearbox according to the gear meshing position, it is greatly influenced by machine operating conditions. In this study, domain-specific features from the HDMap are further removed, while the fault-related features are enhanced. Autoencoder-based residual analysis and digital image-processing techniques are employed to address the domain-shift problem. The performance of the proposed method was validated under significant domain-shift problem conditions, as demonstrated by studying two gearbox test rigs with different configurations operated under stationary and non-stationary operating conditions. Validation accuracy was measured in all 12 possible domain-shift scenarios. The proposed method achieved robust fault detection accuracy, outperforming prior methods in most cases.

진동 신호 기반 합성곱 신경망을 이용한 다양한 하중 조건의 유성기어박스 고장 진단

김수호(Sooho Kim),김현재(Hyunjae Kim),박정호(Jungho Park),윤병동(Byeng D. Youn) 대한기계학회 2018 대한기계학회 춘추학술대회 Vol.2018 No.12

Since a planetary gearbox have been frequently adapted for rotational system, the fault diagnosis of planetary gearbox has been highly required. In this purpose, the physics-based approaches have been suggested but they have required enough domain knowledge which is time-consuming to achieve. Hence, there have been a lot of attempts based on datadriven approach, especially employing machine learning method to overcome the requirements of domain knowledge. Even though these attempts have shown excellent performance, there is too high randomness on designing the architecture of machine learning. In the same time, the physical explanation of process in machine learning have been required, since it is related with the reliability of result. In this research, the End-to-end One-Dimensional Convolutional Neural Network (EODCNN) is proposed for fault diagnosis of planetary gearbox. In the process of designing architecture, the physical properties are considered to optimized the diagnosis performance and the effects of physical properties are compared. Furthermore, the process of trained model is investigated to discover the physical meaning which bring the reliability on the performance of diagnosis model.

Experimental Study on the Carrier Pinhole Position Error Affecting Dynamic Load Sharing of Planetary Gearboxes

Kim, Jeong-Gil,Park, Young-Jun,Lee, Geun-Ho,Lee, Sang-Dae,Oh, Joo-Young Korean Society for Precision Engineering 2018 International Journal of Precision Engineering and Vol.19 No.6

In gearbox design, small size, light weight, and long service life are important factors that must be considered. The planetary gear train structure has been widely used to realize these characteristics. In this study, the effects of increasing the torque on and changing the rotation direction of a planetary gearbox on the dynamic load sharing among the planet gears were analyzed experimentally using a planet carrier manufactured at an industrial site. When the number of planet gears was even, one pair of planet gears exhibited higher load sharing than the other two pairs among the three pairs of planet gears, where the two gears in each pair faced each other directly, regardless of the rotation direction of the gearbox. In addition, increasing the torque of the planetary gearbox improved the mesh load factor. The mesh load factor varied significantly as to the rotation direction changed, due to the phase change of the carrier pinhole position error.

체적을 고려한 유성기어 장치의 최적설계

정태형(Taehyong Chong),양우열(Wooyeoul Yang),이기훈(Kihun Lee) 한국생산제조학회 2009 한국공작기계학회 추계학술대회논문집 Vol.2009 No.-

The wind turbine gearbox has been increasing the volume and size in order to produce more power. An optimal design for volume of planetary gear is important due to limited space on gearbox. The genetic algorithm is applied for the volume minimization of planetary gear which is used in field. Therefore, the purpose of this paper is studied for optimizing the volume of the planetary gear by the genetic algorithm. In result, it shows that the volume has decreased compared with the existing planetary gear.

풍력발전기 유성기어박스의 진동 변조 특성을 고려한 진동기반 고장 진단 기법 고찰

하종문(Jong M. Ha),박정호(Jungho Park),오현석(Hyunsoek Oh),윤병동(Byeng D. Youn) 대한기계학회 2015 大韓機械學會論文集A Vol.39 No.7

유성 기어박스의 진동기반 고장진단 기법은 조립 및 제작공차와 하중조건에 의해 결정되는 진동변조특성에 따라 성능을 달리하는 특성을 갖는다. 이 논문에서는 풍력발전기에 장착되어 있는 유성 기어박스의 고장을 효과적으로 진단하기 위해 진동 변조특성을 고려한 고장진단기법을 제안하고자 한다. 리샘플링된 진동신호에 대한 대역 필터링을 사용함으로써 유성기어박스의 진동 변조특성을 규명하고자 하였으며, 진동추출 윈도우함수의 최적위치를 선정하여 활용함으로써 가변적 진동 변조현상에서도 강건한 고장진단을 수행할 수 있도록 하였다. 제안된 고장진단기법의 검증을 위해 2kW 급 풍력발전기 테스트베드가 설계되었으며 기어 치 부분파손이 모사 제작되어 기어박스에 장착되었다. The performance of fault diagnostics for a planetary gearbox depends on vibration modulation characteristics, which can vary with manufacturing & assembly tolerance, and load condition. In this paper, a fault diagnostics technique that considers vibration modulation characteristics is proposed for the effective fault detection of planetary gearboxes in wind turbines. For identifying the vibration modulation characteristics in practice, re-sampled vibration signals are processed with narrow band-pass filters. Thereafter, the optimal position of the vibration extraction window is identified for effective detection of faulty signals under the varying vibration modulation characteristics. The proposed diagnostics technique makes it possible to perform robust diagnostics of the planetary gearbox with regard to the changeable vibration modulation effect. For demonstrating the proposed fault diagnostics technique, a 2-kW WT testbed is designed with two DC motors and gearboxes. A faulty gear with partial tooth breakage is machined and assembled into the gearbox.

풍력 발전기 유성기어박스의 진동 변조 효과를 고려한 시간 동기 평균화 기반 고장 진단

하종문(Jong M. Ha),박정호(Jungho Park),윤병동(Byeng D. Youn) 대한기계학회 2014 대한기계학회 춘추학술대회 Vol.2014 No.11

Time synchronous averaging (TSA) enables to extract health related information from noisy vibration signals measured at the planetary gearbox in wind turbines (WTs). However, performance of TSA depends on vibration modulation characteristics which can vary with manufacturing and assembly tolerance. In this paper, TSA considering amplitude modulation effect of vibration signal is proposed for diagnostics of planetary gearbox in WTs. For identification of the amplitude modulation effect in practice, re-sampled vibration signals are processed with narrow band-pass filters. Multiple window functions centered on every ring gear ’s tooth were employed for time synchronous averaging to compensate for effect of varying amplitude modulation effect. Proposed diagnostics framework with multiple windowed TSA makes it possible to perform robust diagnostics of the planetary gearbox toward the changeable vibration modulation effect. For demonstration of the proposed TSA, a 2kW WT testbed was designed with two DC motors and gearboxes. A faulty gear with tooth breakage was machined, and assembled into the gearbox.