Planetary Gearbox Research Articles

Phenomenological vibration models of planetary gearboxes for gear local fault diagnosis

Planetary gearboxes (PGs) are prone to gear local faults, such as cracking, pitting, and spalling, because of their difficult working environment. These faults may lead to significant capital losses and catastrophic events. Numerous phenomenological modulation models of vibration signals of PGs have been proposed for diagnosis purposes. However, local fault diagnosis remains extremely challenging due to two aspects: (1) Most vibration modulation models cannot reflect the local fault-induced pulse modulation characteristic. (2) Studies on local fault characteristic frequencies (LFCFs) of gears lack a unified mechanism explanation. To solve these problems, an improved phenomenological vibration model of a gear pair with local fault is first proposed. This model can reflect the pulse modulation characteristic. Then, relative phases between multiple gear pairs in the PG are calculated based on a modified phase model. Based on these, phenomenological vibration models of PGs with/without local faults are established, spectral structures are derived using Fourier series analysis, and LFCFs are deduced for local fault diagnosis. Simulation and experimental studies demonstrate the effectiveness of the proposed models, which exhibit higher descriptive capability than traditional models.

Investigation on planetary gearbox fault mechanism under variable speed conditions based on rigid-flexible coupling dynamics model

Dynamic behaviors of wind turbine planetary gears are greatly affected by the transfer paths and variable conditions. However, previous studies have mainly considered single transfer path and stationary operating conditions. To overcome this problem, a rigid-flexible coupling dynamics model of wind turbine planetary gearbox under different transfer paths and variable conditions is established. By simulating the modal-based rigid-flexible coupling dynamics model, the gearbox output shaft speed as well as the box vibration response in the stationary condition are obtained well, which can be characterized by its frequency features. By conducting order envelope analysis on the simulated acceleration signals in the variable speed condition, the feature order in the sun gear fault state is obtained on the simulated acceleration signal. The planetary gearbox dynamics tests results are in good agreement with the simulation results. The experiment results verify the correctness of the established rigid-flexible coupling dynamics model. This modeling method provides a new idea for multi-path modeling of wind turbine gearboxes and the study of fault mechanism under variable speed conditions.

Degree of cyclic target protrusion defined on squared envelope spectrum for rotating machinery fault diagnosis

Rotating machinery fault diagnosis is critical for safe operation of modern mechanical equipment so as to avoid serious damage and economic loss caused by malfunctions. Envelope analysis is an effective method in the field of rotating machinery fault diagnosis, in which the selection of optimal demodulated band containing the most obvious fault features by fault index is a critical step. However, the existing fault indexes are often defined on the basis of global ratio in their domains of definitions, which may result in incorrect band selection. To address the problem, a novel index named degree of cyclic target protrusion (DCTP) for fault diagnosis is proposed in this paper. DCTP is defined on squared envelope spectrum to take adequately fault characteristic frequency (FCF) into consideration. Specifically, Otsu method is utilized to calculate local threshold for determining cyclic target amplitude and cyclic background in two elaborate bands. In this aspect, DCTP is defined as a weighted local ratio of cyclic target amplitude to cyclic background. Because it focuses on FCF, DCTP can avoid interference by extraneous components. DCTP is used to replace the kurtosis index of fast kurtogram (FK) to establish the DCTPgram which is compared with FK and ratio of cyclic content based method in roller bearing fault diagnosis and planetary gearbox fault diagnosis. Experimental results and comparison studies demonstrate that the DCTPgram has a significant advantage for rotating machinery fault diagnosis.

Feature extraction using Discrete Wavelet Transform for fault classification of planetary gearbox – A comparative study

Monitoring the condition of unreachable gears of epicyclic gearbox in real-time increases the asset reliability by anticipating the failures through preventive maintenance. Machine vibrations exhibit fruitful information about the failure at different operating conditions when measured at a convenient location through signal processing techniques. This study demonstrates the potency of discrete wavelet analysis for fault diagnosis of the planetary gearbox using Artificial Neural Network and Support Vector Machine. The mean squared energy of the detail coefficients at eight decomposed levels for different wavelet families is considered a feature. The J48 algorithm selects the prominent features which form an input to the classifiers. Afterward, their relative ability in classifying faults is compared in detail. Further, the classification performance of commonly used machine learning algorithms in the literature is also compared. It is found that the mean square energy of detailed coefficients of Discrete Wavelet Transform exhibit excellent fault diagnosing characteristics with various classifiers and is highly recommended.

Establishment and analysis of nonlinear frequency response model of planetary gear transmission system

Abstract. Based on the lumped parameter theory, a nonlinear bending torsion coupling dynamic model of planetary gear transmission system was established by considering the backlash, support clearance, time-varying meshing stiffness, meshing damping, transmission error and external periodic excitation. The model was solved by the Runge–Kutta method, the dynamic response was analyzed by a time domain diagram and phase diagram, and the nonlinear vibration characteristics were studied by the response curve of the speed vibration displacement. The vibration test of the planetary gearbox was carried out to verify the correctness of frequency domain response characteristics. The results show that the vibration response in the planetary gear system changes from a multiple periodic response to a single periodic response with the increase in input speed. Under the action of the backlash, time-varying meshing stiffness and meshing damping, the speed vibration displacement response curves of internal and external meshing pairs appear to form a nonlinear jump phenomenon and have a unilateral impact area, and the system presents nonlinear characteristics. The nonlinear vibration of the system can be effectively suppressed by decreasing the mesh stiffness or increasing the mesh resistance, while the vibration response displacement of the system increases by increasing the external exciting force, and the nonlinear characteristics of the system remain basically unchanged. The backlash is the main factor affecting the nonlinear frequency response of the system, but it can restrain the resonance of the system in a certain range. The spectrum characteristics of the vibration displacement signal of the planetary gearbox at different speeds are similar to the simulation results, which proves the validity of the simulation analysis model and the simulation results. It can provide a theoretical basis for the system vibration and noise reduction and a dynamic structural stability design optimization.

Incipient fault diagnosis of planetary gearboxes based on an adaptive parameter-induced stochastic resonance method

The stochastic resonance (SR) method is commonly used in incipient fault diagnosis to extract weak fault features from complex diagnostic signals. However, the extraction effect of a SR system is highly dependent on the choice of system parameters as well as the complexity of input signals. To solve this problem, the present study proposes an adaptive parameter-induced SR method, in which high pass filter and Teager energy operator (TEO) are combined to pre-process the original signal while the grasshopper optimization algorithm is introduced to optimize the SR system parameters. The proposed method is employed to diagnose a set of experimental vibration signals of a planetary gearbox with incipient localized root crack and incipient distributed surface wear as well, leading to satisfactory diagnosis results. Comparing with the existing adaptive stochastic resonance methods, the present method claims the merits of a high signal-to-noise ratio and low computation cost.

Vibration analysis of planetary gearbox using empirical mode decomposition and automatic fault prediction using artificial neural network

The planetary gearbox works on an epicyclic gear train consisting of sun gear meshed with planets gears and ring gear. It got advantages due to its large torque to weight ratio and reduced vibrations. It is mostly employed in analog clocks, automobile automatic gearbox, Lathe machines, and other heavy industries. Therefore, it was imperative to analyze the various faults occurring in a gearbox. Furthermore, come up with a method so that failures can be avoided at the early stage. It was also a reason why it became the field of intensive research. Moreover, the technology of neural networks emerged recently, where machine learning models are trained to detect uneven vibrations on their own. This attracted many researchers to perform the study to devise their own methods of prediction. The central concept of fault prediction by the neural network without human beings’ interference inspired this study. Most industries always wanted to know if their operation line is working fine or not. In this study, an attempt was made to apply the method of deep learning on one of the most critical gearboxes because of its components and functionality. A significant part of the study also involved filtering the vibration data obtained while testing. Comparative analysis of the variation of the peak of acceleration was performed for healthy and faulty conditions.

Optimization of two-speed planetary gearbox with brakes on single shafts

This paper deals with configurations of complex planetary gear trains consisting of two planetary gear trains of basic type. These gear trains are formed by linking shafts from different component gearsets and contain two carriers, and therefore designated as two-carrier planetary gear trains with four external and two coupled shafts. The structural configurations are pointed out, and additional research has been made into gear trains using coupled external shafts for torque input and output, with the controlling brakes acting on single external shafts. The kinematic schemes have been created for all analyzed PGT variants, and the available transmission ratio ranges calculated for both speeds. The transmission ratio is changed by alternating the activation of each brake, enabling their use as transmissions with two transmission ratios in transportation technology and other engineering applications. Extreme transmission ratio changes have been determined for each analyzed PGT design solution. Also, relations of ideal torque ratios to the required transmission ratios of component planetary gear trains for both speeds have been calculated. These relations enable the selection of compound gear train designs which will achieve the required pair of transmission ratios. The optimal design parameters for the adopted configuration were determined, and the optimal transmission solution for the given input data selected.

A bidirectional and low-frequency energy harvester for collecting human crowd energy in shopping malls

Human crowd kinetic energy is a promising electricity source in shopping malls. Harvesting human crowd energy can supply considerable power for lighting and door control. In this study, a bidirectional low-frequency energy harvester was proposed, analysed, fabricated, and tested. The harvester was mainly composed of a hinge, four one-way bearings, one sun gear, one internal gear, one planetary gearbox and one direct current motor. The hinge was used to connect the door. The combination of these one-way bearings, hollow shaft, solid shaft and gears transformed the bidirectional movement into unidirectional movement. The utilization of the planetary gearbox boosted the velocity of the door movement to overcome the low-frequency problems. The motor generated a varied direct current voltage. A theoretical analysis was performed to ascertain the factors that would determine the energy harvester’s performance. A full-size prototype was fabricated, a simulated shopping door environment was created, and experiments were investigated. The simulation results correlated well with the experimental results. At an ultralow door movement (45°/s), the harvester can output an open-circuit voltage of 7.4 V (steady voltage) and a power of 3046 mW. The harvester had a high efficiency up to 56.79%. The harvested voltage can power electronic locks, LED billboards and wireless doorbells, all of which are necessary apparatuses in shopping malls. The proposed harvester can potentially be used in shopping malls to harvest plentiful human crowd kinetic energy.

An Enhanced Multifeature Fusion Method for Rotating Component Fault Diagnosis in Different Working Conditions

Mechanical rotating components such as bearing and gear are widely used in various industrial occasions. Fault diagnosis of rotating component can guarantee operational reliability and reduce maintenance cost of system. Although many fault diagnosis methods have been developed based on artificial intelligence methods, most of them ignore the existing signal processing knowledge in rotating element fault diagnosis domain. Meanwhile, these methods just assume that working conditions such as load or rotating speed keep constant, which may cause low accuracy once operating condition changes. To address these problems, an enhanced intelligent fault diagnosis method for rotating component is proposed based on multifeature and convolutional neural network (CNN). The vibration signals are first transformed into angular domain by resampling technique. Then, the angular domain signals are converted to obtain corresponding envelope and squared envelope spectrum features, which are fused into red–green–blue color image form to enhance sample features and enlarge differences among various health states. Finally, a CNN is constructed to accomplish fault recognition. Experimental results show that the average diagnosis accuracies of planetary gearbox and rolling element bearing datasets can reach 96.7% and 95.65%, which demonstrate that the multifeature approach is effective in fault diagnosis of rotating components in scenarios with different rotating speeds.

Wheel drive with integrated differential

The performance indicators of wheeled arable machine-tractor units, which are accelerated on the working gear, depend on the operating modes of the wheels during this period. When the wheel is skidding, soil lumps break down in the contact spot, the soil structure is destroyed. Based on the system analysis of the wheels operation, the method of their improvement is justified by continuous control of the eccentric point of application of the driving torque and external load. As a result of the analysis for the first time, a soil-sparing wheel mover with the properties of a differential, a tangential force regulator and clearance regulator was developed. In the case of an eccentric application of a vertical load and a longitudinal pushing force, one of the satellites of the wheeled planetary gearbox is the leading and bearing one. The purpose of the article is to analyze the factors influencing the automatic adaptation of the wheel drive to changing operating conditions. It is established the relationship between the driving moment and the rolling resistance moment, the moments of inertia of the wheel and the drive gear of the integrated differential.

Kinematics and dynamics of an incomplete circular wheel drive of an agricultural tractor

The disadvantage of wheeled tractors is soil compaction, slipping due to limited traction, low tangential force. Experimental studies of a tractor with incomplete circular wheel mover on stubble, sand and virgin snow showed an increase in cross-country ability, a decrease in skidding, an increase in traction, and an increase in productivity. The purpose of the study is to develop a methodology for kinematic and dynamic analysis of incompletely rounded wheel propellers with a built-in differential. The equation of motion of the wheel is obtained on the basis of two-stage overcoming by the wheel of a single threshold obstacle taking into account the longitudinal and radial stiffness of the tire, its deformation, air resistance in the tire. The main influence is provided by translational speed, wheel radius and radial stiffness, the moment of inertia of the wheel and the shoulder of the application of mass. Planetary gearbox proposed in which the shaft of the driving satellite is a bearing, while the radius of the gear is an order of magnitude smaller than the radius of the wheel. The direction of improvement of wheel mover, increasing their traction properties is justified.

NEW TEST RIG FOR MEASURING THE FRICTION TORQUE OF GEARBOXES IN EXTREME THERMAL CONDITIONS

A new test rig for tribological tests was developed and manufactured. It consists of a mobile device for measurement of the start-up friction torque of transmissions, in particular planetary gearboxes, and the friction torque in dynamically steady conditions, as well as a climatic chamber to stabilize the temperature of the tested gearbox in its extreme range: from -50 to +50°C. In the series of devices for tribological tests, developed and manufactured at the Institute, the new test rig is marked with the symbol T-34. The verification results correspond with the churning losses related to the viscosity characteristics of the lubricating oils. As the temperature increases, both the start-up friction torque and the friction torque under dynamically steady conditions decrease.

Intelligent Fault Diagnosis Method of Wind Turbines Planetary Gearboxes Based on a Multi-Scale Dense Fusion Network

Due to the powerful capability of feature extraction, convolutional neural network (CNN) is increasingly applied to the fault diagnosis of key components of rotating machineries. Due to the shortcomings of traditional CNN-based fault diagnosis methods, the continuous convolution and pooling operations result in the constant decrease of feature resolution, which may cause the loss of some subtle fault information in the samples. This paper proposes a CNN-based model with improved structure multi-scale dense fusion network (MSDFN) to realize the fault diagnosis of wind turbines planetary gearboxes under complicated working conditions. First, the continuous wavelet transform is applied to preprocess the vibration signals, and the two-dimensional wavelet time-frequency diagrams are used as the network input. Then, the multi-scale feature fusion (MSFF) module and a feature of maximum (FoM) module are used in the extraction and classification stages of fault features, respectively. Next, the multi-scale features of each network layer are fused to enhance the fault features. Finally, the high fault diagnosis accuracy is achieved by extracting the separable fusion result of fault features. The proposed method achieves more than 99% fault diagnosis average accuracy on a planetary gearbox dataset. The comparative experimental results verify the effectiveness of the proposed method and its superiority to some mainstream approaches. The ablation study further confirms that MSFF module and FoM module play the positive role in fault diagnosis.

A health-adaptive time-scale representation (HTSR) embedded convolutional neural network for gearbox fault diagnostics

This research proposes a newly designed convolutional neural network (CNN) for gearbox fault diagnostics. A conventional CNN is a deep-learning model that offers distinctive performance for analyzing two-dimensional image data. To exploit this ability, prior work has been developed using time–frequency analysis, which derives image-like data that is fed into the CNN model. However, the existing time–frequency analysis approach employs fixed basis functions that are limited in their ability to capture fault-related signals in the image. To address this challenge, we propose a health-adaptive time-scale representation (HTSR) embedded CNN (HTSR-CNN). The proposed HTSR approach is designed to exploit the concept of TSR, which is informed by the physics of the time and frequency characteristics induced by the fault-related signals. Instead of using fixed basis functions, the HTSR is constructed using multiscale convolutional filters that behave like the adaptive basis functions. These multiscale filters are effectively learned to include the enriched fault-related information in the HTSR through end-to-end learning of the HTSR-CNN model. The performance of the proposed HTSR-CNN is validated by examining two case studies: vibration signals from a two-stage spur gearbox and vibration signals from a planetary gearbox. From the case study results, the proposed HTSR-CNN method is found to have superior performance for gearbox fault diagnostics, as compared to existing CNN-based fault diagnostic methods.

Dynamic behaviour of a planetary reducer with double planet gears

Abstract. Planetary reducers have a wide and significant application in modern machine industry. They are extremely compact and reliable, and they can achieve a wide range of gear ratios as well as high efficiency. Since they often operate in extremely changeable working conditions, such as often starting and stoping, it is both interesting and desirable to study their dynamic behaviour. This paper presents a dynamic model of a planetary reducer with double planet gears created by applying Lagrange's equations of the second kind. After a planetary gear reducer has been designed and a CAD model created, the dynamic model was made. Most of characteristic values needed for solution of the equations are taken from the CAD model. Due to their complexity, the equations are solved in math simulation software. A lot of interesting results concerning the dynamic behaviour of the model are obtained, which can be helpful in understanding the dynamic occurrences in the inside of a planetary gearbox. At the end of the paper, conclusions are drawn, and further research directions in this area are given.

An image-based feature extraction method for fault diagnosis of variable-speed rotating machinery

This paper proposes a new feature extraction method using time–frequency image data for fault diagnosis of variable-speed rotating machinery. Time-frequency representation (TFR) is widely used to analyze time-varying behaviors of rotating machinery. Recently, methods have been developed to extract fault-related features from TFR image data. However, these methods can be only applied to in-phase TFR image data, or have limited sensitivity because they cannot utilize the characteristics of faults in rotating machinery. Therefore, the research outlined in this paper proposes a new fault feature for rotating machinery under variable-speed conditions. The proposed feature enhances sensitivity by exploiting faulty behaviors in the TFR image data. Two experimental case studies are presented to demonstrate the performance of the proposed method: a planetary gearbox and a spur gearbox. From the results, we conclude that the proposed method shows higher fault sensitivity than the previous image-based features, while showing consistent behavior under different phases of TFR image data.

Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox

Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data.

Planetary Gearbox Prototype Development and Manufacturing

Goal of this research was to develop and manufacture planetary gearbox prototype using rapid prototyping technology (additive manufacturing). Developed prototype was used to visually analyse the design of the planetary gearbox. Also, it was used to improve and innovate education of students on several courses at Mechanical Design study program at Faculty of Mechanical Engineering. It is shown that low cost rapid prototyping technology can be used to manufacture prototypes of complex machines and machine elements. Prototypes manufactured using this technology have same functionality like the real one. Main limitation is the fact that they cannot sustain real world loads and stresses. This paper shows opportunities which low cost rapid prototyping technology is offering in improvement and innovation of education process at engineering schools and faculties. All complex and heavy machines can be manufactured using this type of technology and on that way more precisely presented to the students.

Влияние точности изготовления планетарно-цевочного редуктора на точность позиционирования робота параллельной структуры

The increased accuracy in high-speed positioning of the parallel robot effector in comparison with that of serial robots with a sequential structure is often the main reason for their use in various modern industries, such as the manufacture of printed circuit boards for microelectronics. However, despite the higher theoretical positioning accuracy, due to the kinematic structure of the parallel robot, in practice this characteristic largely depends on the accuracy of manufacturing individual elements of this mechanism, the most important of which are the gearboxes of the drives of its input pairs. A solution to the urgent problem of determining the effect of the manufacturing accuracy of planetary pinion gearboxes included in the drive of a five-link parallel robot on the positioning accuracy of its output link is proposed. A specific relationship has been determined between the grade of accuracy number of the gear part dimensions and the robot positioning accuracy. The unevenness of the positioning accuracy along the coordinate axes of its working area is revealed. It was found that near the area of certain robot positions the accuracy of its positioning drops sharply.