Electromechanical modeling of a motor–gearbox system for local gear tooth faults detection

The effects of tooth modification on the nonlinear dynamic behaviors are studied in this paper. Firstly, the static transmission error under load combined with misalignment error and modification are deduced. These effects can be introduced directly in the meshing stiffness and static transmission error models. Then the effect of two different type of tooth modification combined with misalignment error on the dynamic responses are investigated by using numerical simulation method. The numerical results show that the misalignment error has a significant effect on the static transmission error. The tooth crowning modification is generally preferred for absorbing the misalignment error by comparing with the tip and root relief. The tip and root relief can not resolve the vibration problem induced by misalignment error but the crowning modification can reduce the vibration significantly.

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Influence of gear tooth addendum and dedendum on the helical gear optimization considering mass, efficiency, and transmission error

Mechanism and Machine Theory ◽

10.1016/j.mechmachtheory.2021.104476 ◽

2021 ◽

Vol 166 ◽

pp. 104476

Author(s):

Chanho Choi ◽

Hyoungjong Ahn ◽

Young-jun Park ◽

Geun-ho Lee ◽

Su-chul Kim

Keyword(s):

Gear Tooth ◽

Transmission Error ◽

Helical Gear

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The Structure and Optimal Gear Tooth Profile Design of Two-Speed Transmission for Electric Vehicles

Energies ◽

10.3390/en14133736 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3736

Author(s):

Jae-Oh Han ◽

Won-Hyeong Jeong ◽

Jong-Seok Lee ◽

Se-Hoon Oh

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Automobile Industry ◽

Bending Strength ◽

Gear Tooth ◽

Planetary Gear ◽

Maximum Speed ◽

Compact Structure ◽

Simple Method ◽

Paris Climate Agreement

As environmental regulations have been strengthened worldwide since the Paris Climate Agreement, the automobile industry is shifting its production paradigm to focus on eco-friendly vehicles such as electric vehicles and hydrogen-battery vehicles. Governments are banning fossil fuel vehicles by law and expanding the introduction of green vehicles. The energy efficiency of electric vehicles that use a limited power source called batteries depends on the driving environment. Applying a two-speed transmission to an electric vehicle can optimize average speed and performance efficiency at low speeds, and achieve maximum speed with minimal torque at high speeds. In this study, a two-speed transmission for an electric vehicle has been developed, to be used in a compact electric vehicle. This utilizes a planetary gear of a total of three pairs, made of a single module which was intended to enable two-speed. The ring gear was removed, and the carrier was used in common. When shifting, the energy used for the speed change is small, due to the use of the simple method of fixing the sun gear of each stage. Each gear was designed by calculating bending strength and surface durability, using JGMA standards, to secure stability. The safety factor of the gears used in the transmission is as follows: all gears have been verified for safety with a bending strength of 1.2 or higher and a surface pressure strength of 1.1 or higher. The design validity of the transmission was verified by calculating the gear meshing ratio and the reference efficiency of the gear. The transmission to be developed through the research results of this paper has a simple and compact structure optimized for electric vehicles, and has reduced shift shock. In addition, energy can be used more efficiently, which will help improve fuel economy and increase drive range.

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Modeling of surface roughness in a novel magnetorheological gear profile finishing process

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220978265 ◽

2020 ◽

pp. 095440622097826

Author(s):

Ravi Datt Yadav ◽

Anant Kumar Singh ◽

Kunal Arora

Keyword(s):

Surface Roughness ◽

Gear Tooth ◽

Surface Characteristics ◽

Spur Gear ◽

Tooth Profile ◽

Tooth Surface ◽

Magnetic Flux Density ◽

Element Analysis ◽

Finishing Process ◽

Gear Profile

Fine finishing of spur gears reduces the vibrations and noise and upsurges the service life of two mating gears. A new magnetorheological gear profile finishing (MRGPF) process is utilized for the fine finishing of spur gear teeth profile surfaces. In the present study, the development of a theoretical mathematical model for the prediction of change in surface roughness during the MRGPF process is done. The present MRGPF is a controllable process with the magnitude of the magnetic field, therefore, the effect of magnetic flux density (MFD) on the gear tooth profile has been analyzed using an analytical approach. Theoretically calculated MFD is validated experimentally and with the finite element analysis. To understand the finishing process mechanism, the different forces acting on the gear surface has been investigated. For the validation of the present roughness model, three sets of finishing cycle experimentations have been performed on the spur gear profile by the MRGPF process. The surface roughness of the spur gear tooth surface after experimentation was measured using Mitutoyo SJ-400 surftest and is equated with the values of theoretically calculated surface roughness. The results show the close agreement which ranges from −7.69% to 2.85% for the same number of finishing cycles. To study the surface characteristics of the finished spur gear tooth profile surface, scanning electron microscopy is used. The present developed theoretical model for surface roughness during the MRGPF process predicts the finishing performance with cycle time, improvement in the surface quality, and functional application of the gears.

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Measurement and modeling methods of grinding-induced residual stress distribution of gear tooth flank

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07392-w ◽

2021 ◽

Author(s):

Yuliang Xiao ◽

Shilong Wang ◽

Chi Ma ◽

Sibao Wang ◽

Lili Yi ◽

...

Keyword(s):

Residual Stress ◽

Stress Distribution ◽

Gear Tooth ◽

Residual Stress Distribution ◽

Modeling Methods ◽

Tooth Flank

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Straddle Design of Spiral Bevel and Hypoid Pinions and Gears

Journal of Mechanical Design ◽

10.1115/1.2912799 ◽

1991 ◽

Vol 113 (4) ◽

pp. 422-426 ◽

Cited By ~ 4

Author(s):

F. L. Litvin ◽

C. Kuan ◽

J. Kieffer ◽

R. Bossler ◽

R. F. Handschuh

Keyword(s):

Free Space ◽

Gear Tooth ◽

Main Difficulty ◽

Hypoid Gears ◽

Numerical Example ◽

Cone Apex ◽

Spiral Bevel

The design of spiral bevel and hypoid gears that have a shaft extended from both sides of the cone apex (straddle design) is considered. A main difficulty of such a design is determining the length and diameter of the shaft that might be undercut by the head cutter during gear tooth generation. A method that determines the free space available for the gear shaft is proposed. The approach avoids collision between the shaft being designed and the head cutter during tooth generation. The approach is illustrated with a numerical example.

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Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽

10.3390/e23080975 ◽

2021 ◽

Vol 23 (8) ◽

pp. 975

Author(s):

Yancai Xiao ◽

Jinyu Xue ◽

Mengdi Li ◽

Wei Yang

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbines ◽

Gear Tooth ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Empirical Wavelet Transform ◽

Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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