Gear Wear Research Articles

Fault mechanism and dynamic modeling of planetary gear with gear wear

Gear wear is a typical and common early failure, which could lead to geometric deviation of tooth surface, further affecting vibration signal and transmission error. This paper is devoted to analyzing fault mechanism and dynamic characteristics of gear wear, which provides the basis for vibration-based fault diagnosis and monitoring of gear wear. For this purpose, a purely torsional dynamic model of planetary gear with gear wear is proposed where gear wear is evaluated by Archard's equation and it is incorporated into the dynamic model through time-varying mesh stiffness, unloaded static transmission error and gear backlash. Inspired by the work of Chen et al., gear wear is viewed as a kind of tooth deviation and the influence of gear wear on planetary gear dynamic parameters can be analyzed. Finally, the vibration responses of planetary gear under the effect of gear wear are analyzed.

Use of cyclostationary properties of vibration signals to identify gear wear mechanisms and track wear evolution

Fatigue pitting and abrasive wear are the most common wear mechanisms in lubricated gears, and they have different effects on the gear transmission system. To develop effective methods for online gear wear monitoring, in this paper, a vibration-based wear mechanism identification procedure is proposed, and then the wear evolution is tracked using an indicator of vibration cyclostationarity (CS). More specifically, with consideration of the underlying physics of the gear meshing process, and the unique surface features induced by fatigue pitting and abrasive wear, the correlation between tribological features of the two wear phenomena and gearmesh-modulated second-order cyclostationary (CS2) properties of the vibration signal is investigated. Differently from previous works, the carrier frequencies (spectral content) of the gearmesh-cyclic CS2 components are analysed and used to distinguish and track the two wear phenomena. The effectiveness of the developed methods in wear mechanism identification and degradation tracking is validated using vibration data collected in two tests: a lubricated test dominated by fatigue pitting and a dry test dominated by abrasive wear. This development enables vibration-based techniques to be used for identifying and tracking fatigue pitting and abrasive wear.

ANALYSIS OF MICROSCOPIC WEAR ON STRAIGHT GEAR WITH ROTATION VARIATION

Wear is an important factor in reducing the function of engine performance including limiting the service life and performance of various engine components that rub against each other directly, this results in increased maintenance costs. In this study aims to determine the rate of straight gear wear with SCM 440 material from the influence of load and shaft rotation. Experimental research methods with variables of 200 rpm, 500 rpm and 1000 rpm and load respectively 0, 5, 10 kg, from the test results obtained the highest value of power at 1000 rpm and 0 kg load, while for the lowest wear value obtained at 200 rpm and a load of 5 kg, this is due to the misalignment factor in the drive shaft and being driven, this can be seen from the average wear position located at the end of the gear that is in direct contact between the gear and the gear is moved, so that in rotation high and without loading the shaft rotation is not balanced, but at high rotation and with a certain load imbalance rotation due to misalignment can be suppressed and produce a low wear value.

Safety Riding Gear Wearing Status among Industrial Workers Commute to Work in Bangi: A Baseline Study


 
 
 In Malaysia, motorcyclists have been known to record the highest fatality rate in road accidents every year. On average, around 12 motorcyclists and pillion riders die each day in road accidents. This is a cross-sectional study conducted among workers riding to work and return from work in selected manufacturing companies in Bangi. Results show the wearing rate of safety helmet and vest among selected companies that participated shows high compliance of helmet wearing while the wearing rate of safety vest was very low, less than 4%. Most of the riders wore the jacket during riding (70.5%) however it was dark colour (74%) and not a proper riding jacket. Last but not least, about 71% of rider wore proper footwear to their workplace rather than slipper. Full commitment from employer and employee is a must to create a safety culture among riders.
 
 

Real-time residual life prediction based on kernel density estimation considering abrupt change point detection

The existence of abrupt change points in mechanical equipment degradation leads to inaccuracies in the prediction of its residual life. We propose a real-time residual life prediction method based on kernel density estimation (KDE) considering the influence of abrupt change points. First, a non-parametric cumulative sum method is used to detect abrupt change points in the degradation process. Then, integral mean square error is used to determine the abrupt change in the sample number that affects the accuracy of KDE. The weight coefficient is adaptively allocated according to the change in sample numbers relative to the minimum sample number before and after the abrupt change point in real-time monitoring. This method considers abrupt change states in the degradation process and uses KDE, which does not make model structure assumptions or parameter estimations for the degradation process. Finally, the effectiveness of this method is verified using the gear wear degradation data and compared with the real-time residual life prediction method based on KDE without considering the impact of the abrupt change point. The results show that this method can track the dynamic changes of a system more quickly and improve the accuracy of real-time residual life prediction.

Study on the gear performance of polymer-clay nanocomposites by applying step and constant loading schemes and image analysis

The efficiency of polymer nanocomposites for gear applications is evaluated in this paper. Poly(butylene terephthalate) (PBT) and montmorillonite (MMT) were employed as the polymer matrix and nanoreinforcement, respectively. Gear tests were conducted under 6 and 11 Nm constant torques, determined via step-loading approach. Image processing was employed to calculate gear tooth wear, identify wear patterns, and explore failure mechanisms. The inclusions of MMT reduced tooth surface temperature and wear, at identical durations, relative to neat PBT. The average gear lifespan increased up to 107% and 79% at 6 and 11 Nm loads, respectively, with clay addition. Tooth profile deviation was suppressed for PBT/MMT gears, in contrast to neat PBT, at both applied loads. At 6 Nm, fatigue-induced pitch cracking was predominant for both pristine and nanocomposite gears, while the failure mode at 11 Nm was altered from thermal bending for pure PBT to crack-induced fracture for PBT/MMT gears. The microstructural evaluation of worn teeth indicated the transition of wear mechanism from adhesive for pure PBT to abrasive with smoother worn surface for PBT/MMT gears. Digital image correlation (DIC) was applied to obtain the strain distribution of deformed gear teeth and study its correlation with the applied torque and material composition.

Examination on the wear process of polyformaldehyde gears under dry and lubricated conditions

This study presents the results of detailed wear process examination on polyformaldehyde gears under both dry and lubricated conditions. A multi-purpose durability test rig was employed to study the wear performance of polyformaldehyde gear pairs. The wear behaviors of polyformaldehyde gears under dry and oil-lubricated operating conditions were characterized via measurements of gear tooth surface micro-topography and tooth profile deviation. Under the dry running condition, a hump and a gully appear on the tooth surface in the pitch line area of the driving gear and the driven wheel, respectively. The largest amount of wear was observed around the tooth root of the driving gear. However, the gear tooth wear pattern with lubrication is different from that under the dry running condition.

Classification of stages of wear in spur gears based on wear debris morphology

This work investigates the morphological changes in the wear debris, generated during the different stages of gear wear. Wear debris are generated at the mating load-bearing tooth surfaces having relative motion. The number and size of collected wear debris provide useful information for the gear fault diagnosis.
 In the present work, both online and offline analyses of wear debris are carried out for gear fault diagnosis. In the online analysis, the oil from the gear sump is passed through online wear debris counter (to estimate the number of wear particles per minute) and particle size bin. Along with the online process, the oil samples are collected periodically, and wear debris particle images are captured using a scanning electron microscope (SEM). These images are subsequently processed to determine the parameters related to the shape, size and boundary features of the particles. The results of the modified texture in different stages of gear wear are reported. The average wear mass calculated as the actual area of the wear particles is combined with particle per minute is used as the stage’s classification.
 The combined online and offline study provides a better prediction of mild wear progression along with the information on the wear mechanisms at different stages of wear. The presence of different type of particles (ferrous and non-ferrous) points to degradation of specific components.

Investigation on the wear of spur gears generated by modified cutter

Tooth surface wear damage is one of the main causes of gearing system failure. Excessive wear leads to tooth profile loss and an increase in transmission errors, as the worn gear surfaces are no longer conjugate. Thus, the enhancement of gear durability against wear is important for gear application. Recent works show that cutter modification can aid in reducing the tool wear in gear processing, while the wear performance of the gears produced by modified cutters is still unknown. Therefore, this study focuses on the wear performance of the gear generated by modified cutter. Numerical results show that the wear resistance can be enhanced through proper cutter modification.

Absolute transmission error: A simple new tool for assessing gear wear

Gear transmission error (TE) has become a promising diagnostic tool due to the fact that, compared with vibration responses, it is much less affected by the transfer path from the source to the measurement, and it can be measured conveniently using shaft encoders installed on the input and output of a gear stage. It is also the direct cause of vibration and noise in gears, and it is directly linked to deviations from the perfect involute tooth profile. In the literature, TE-based gear diagnostics has revolved around the analysis of changes in the gearmesh waveform, such as its amplitude or shape, which contains information about the tooth profile and deflection under load. However, if there is a bulk removal of material from the tooth surface due to wear, appearing as a mean effect across all teeth, this would in theory manifest itself as a DC shift in the transmission error, but it would not be observed in a conventional TE signal, even though it is essentially the most crucial measurement that describes wear severity. This is because the initial phases of the encoder signals used to obtain conventional TE measurements are arbitrary, and consequently the mean of TE signals is usually set to zero since it does not represent any physical quantity. Therefore, to be able to measure the effect of average wear depth on TE (mean TE), the TE signals recorded at different wear stages must somehow be rephased so as to align them to have the same fixed-reference starting point before they can be compared. This paper proposes a novel approach to conduct this rephasing of encoder signals (or even once-per-rev tachometer signals) to have the same reference within a hunting tooth period (HTP), defined as the fundamental period where the meshing of all different tooth pairs has been encountered. This rephasing enables the determination of the DC shift in the transmission error relative to a reference measurement, typically from an unworn gearset. In the case of multi-pulse-per-rev encoder signals, this DC component can be combined with the conventional TE to obtain the ‘absolute transmission error’, which indicates not just deviations from an involute profile, but also the average wear depth for all teeth in the gear set. The proposed technique is validated using encoder and tachometer measurements from a dry gear wear experiment and is found to be very robust in assessing the severity (average depth) of gear wear.

Influence of tip relief in spur gears dynamic using multibody models with movable teeth

Tip relief modifications are a common design feature used in industrial practice to reduce dynamic tooth loading, noise, vibrations and wear in high-performance gears. This paper presents a novel implementation of a specific multibody model to include the effect of tip relief micro-modification on spur gears. The model considers every tooth attached to the wheel hub with a revolute joint and an equivalent torsion spring. A penalty formulation is adopted to solve the teeth contact (2D). The proposed methodology offers a trade-off between the accuracy of an equivalent finite element method model and the advantage of reducing the degrees of freedom allowing for fast dynamic simulations. Moreover, there is no need to preliminary predict mesh stiffness or static transmission error as a source of excitation for dynamic simulation, because these are direct outputs of the model simulation given the modified tooth geometry. Model validation is done comparing numerical results with experimental data taken from literature. The effects of geometry modification are studied both in quasi-static and dynamic conditions. Finally, the model has adopted to assess the off-design performance of spur gears.

Monitorowanie stanu i wykrywanie błędów przekładni ślimakowej na podstawie termogramów z wykorzystaniem metody głębokiego uczenia

Worm gearboxes (WG) are often preferred, because of their high torque, quickly reducing speed capacity and good meshing effectiveness, in many industrial applications. However, WGs may face with some serious problems like high temperature at the speed reducer, gear wearing, pitting, scoring, fractures and damages. In order to prevent any damage, loss of time and money, it is an important issue to detect and classify the faults of WGs and develop the maintenance plans accordingly. The present study addresses the application of the deep learning method, convolutional neural network (CNN), in the field of thermal imaging that were gathered from a test rig operating on different loads and speeds. Deep learning approaches, have proven their powerful capability to exploit faulty information from big data and make intelligently diagnostic decisions. Studies concerning the condition monitoring of WGs in the literature are limited. This is the first study on WGs with infrared thermography rather than vibration and sound measurements which have some deficiencies about hardware requirements, restricted measurement abilities and noisy signals. For comparison, CNN was also trained, with vibration and sound data which were collected from the healthy and faulty WGs. The results of fault diagnosis show that thermal image based CNN model on WG has achieved 100% success rate whereas the vibration performance was 83.3 % and sound performance was 81.7%. As a result, thermal image based CNN model showed a better diagnosing performance than the others for WGs. Moreover, condition monitoring of WGs, can be performed correctly with less measurement costs via thermal imaging methods.

Study on wear in spur gears based on an improved load distribution model considering the effects of corner contact

Gear surface wear is an unavoidable tooth profile deviation and can influence gear load distribution and transmission error. Because of that, it is necessary to study the interaction between them and wear. For that purpose, a modified wear prediction model has been established by combining the potential energy method, and an improved load distribution model considering the effect of corner contact. The load share ratio (LSR) obtained from the improved model is verified by the finite element method (FEM). And relative to the FEM, the proposed model is much more efficient. Besides, the influences of wear accumulation process and corner contact on loaded static transmission error (LSTE) and load distribution are discussed. The results show that considering the effect of corner contact is necessary and can improve the accuracy of the wear prediction. And, it can be observed that there is a significant time-varying characteristic on LSTE, LSR of worn gear, as well as a correlation between them. Furthermore, the influence of gear modulus and gear ratio is discussed as well and it is found that both can effectively reduce the wear depth and significantly influence the condition of corner contact.

Failure and wear analysis of poly(butylene terephthalate) nanocomposite spur gears

This paper investigates the wear and failure mechanisms in poly(butylene terephthalate) (PBT) nanocomposite gear pairs. An image-based technique was employed to measure the gear tooth wear. Image acquisition process was further used to identify tooth wear patterns and explore gear failure mechanisms. Gear test results revealed that the inclusion of nanofillers increased the load-bearing capacity and wear resistance. Gear failure mode was altered from thermal tooth deflection for PBT to dominant crack-induced tooth fracture for the nanocomposites. Morphology analysis of the worn teeth indicated the transition of wear mechanism from adhesive for pure PBT to abrasive type with the inclusion of nanofillers. Furthermore, the strain field on the gear teeth was analyzed by applying digital image correlation (DIC) technique.

Using signals from the gas-turbine engine shaft speed sensor in the diagnosis of the technical condition of its reduction gearbox

It is shown that analyzing the signals from standard sensors of gas-turbine engine shaft speed we can resolve some issues of diagnostics of the technical condition of the reduction gearbox without installing any additional vibration transducers. It was found, in particular, that the vibration spectrum component generated by the wear of the differential reduction devices gear teeth causing resonant oscillations of the turbocharger blades produces torsional vibration at the appropriate frequency. The analysis of the parameters of the instantaneous rotational speed of the reduction gear output link (the shaft of the rear screw) made it possible to obtain some diagnostic indicators of the value of the reduction gear teeth wear on the basis of evaluation of the frequency deviations variance. It was established that the value of the diagnostic indicator is linearly dependent on the value of wear. The norm for this indicator was established for the engine under investigation. Significant influence of the load on its level was shown.

Fault diagnosis of synchronous hydraulic motor based on acoustic signals

Synchronous hydraulic motors are used in high load conditions. Therefore, the failure of such motors must be promptly detected to avoid severe accidents and economic loss. The automation of signal processing and diagnostic processes in practical engineering applications can help improve engineering efficiency and reduce hazards. As a non-contact acquisition signal, an acoustic signal has easier acquisition than a vibration signal. This article proposes an automatic fault detection method for synchronous hydraulic motors, which uses acoustic signals. The proposed method includes the automatic calculation and pattern recognition of the parameters of fault feature vectors. The automatic calculation of the fault feature vector is based on the combination of wavelet packet energy and the Pearson correlation coefficient. Then, the nearest-neighbor classifier is used for fault diagnosis. This study verifies that the proposed method can effectively identify the normal state, gear wear, gear rust, and barrier block wear. This method provides a solution for the automatic fault diagnosis of synchronous hydraulic motors and other types of quasi-period rotating machinery.

Typical status recognition of gearbox based on big data

In this paper, the experimental data of five different working states of the gearbox during the normal operation and the failure of the gearbox are taken as the research object, the python-based data analysis tools Numpy and Pandas are used to establish a fault detection model, by collecting time-domain and frequency-domain data of several faults such as fracture, crack and wear of transmission gear, and analyzing the characteristic parameters of the data, the classification of transmission faults is realized, thus laying a foundation for the application of big data tools in fault diagnosis and analysis.

Automated assessment of gear wear mechanism and severity using mould images and convolutional neural networks

A novel methodology for automated wear mechanism and severity assessment combining surface replication, imaging and deep learning is proposed. A large dataset of images of gear teeth moulds was built and covers abrasive wear, macropitting and scuffing, and three severity levels for each mechanism, i.e., mild, moderate and severe. A two-level inference methodology was implemented, based on a first convolutional neural network (CNN), which contains multiple convolutional layers and is commonly used for image classification, for wear mechanism identification, followed by three CNNs for wear severity estimation. The first level obtained a test classification accuracy of 98.22% and the second of 95.16% on average. The two-level system was also applied to full tooth flank mould images to generate wear mechanism and severity maps showing the geographical distribution of wear.

Failure analysis of 3D-printed steel gears

The basic principle of additive manufacturing technology is that the model can be manufactured directly via computer-aided design system. This technology is quite different from traditional methods and allows production to avoid wasting material.This study features 420 steel gears produced using both additive and conventional manufacturing. The gears were subjected to identical test processes and compared to one another. The effects of rotational speed, torque, production technique, and post-production surface treatment were investigated. The researchers performed wear loss, efficiency analysis, oil analysis, damage analysis, optical, and scanning microscope surface analysis. It was revealed that the density and hardness of the gears produced by additive manufacturing (Direct Metal Laser Sintering – DMLS) were quite close to those produced by conventional methods (hobbing machine); however, the surface properties were different. Therefore, the types of damage that occur under the same operating conditions differ. As a result, it was found that gears produced by additive manufacturing wear more, but the weight loss (which is a measure of wear) from wear was mostly caused by non-sintered powders. However, under the same operating conditions, it was found that production technique has little effect on efficiency. In some conditions, the performance of the gears produced by additive manufacturing (with a smoother tooth profile) was better from the computer-aided design. It was also seen that surface treatment after additive manufacturing has a preventive effect on gear wear and damage.

An Experimental Investigation on the Wear of Lubricated Steel Against PEEK Gears

Abstract The application of polymer gears in power transmission has been considered recently due to their advantages over metal gears, e.g., lighter weights and lower costs. Much further research work needs to be carried out to understand their wear mechanisms and to establish their design methods. This paper aims to investigate the wear and failure behavior of the poly-ether-ether-ketone (PEEK) gear against the steel gear under oil jet lubrication through the durability test with the standard FZG test rig. The service life and wear loss of PEEK gears were experimentally recorded under different moderate loading conditions ranging from the output torque of 10–20 Nm with a fixed input rotational speed of 1000 rpm. Moreover, the tooth surface morphologies and the wear loss of PEEK gears under different running stages were investigated. The experimental measurements reveal that there is a critical load magnitude for the transition of wear performance. With a load above 15 Nm, the PEEK gear wear rate increases rapidly, leading to a quick breakage failure, whereas the gear wear rate with a torque below 15 Nm is much lower. All tested PEEK gears finally failed with a pitting-induced tooth breakage mode under moderate loading conditions.