Experimental investigations of surface wear assessment of spur gear teeth
TL;DR: In this paper, a pair of spur gears were tested under an accelerated test condition in an oil bath lubricated back-to-back gearbox and the measurement of stiffness was carried out using modal analysis in conjunction with a theor...
Abstract: The most common gear wear mechanisms, such as micropitting, pitting and scouring often occur in the early stage of failure. For the geared system operating in the presence of such surface failures, the load acts only over a finite region and contact stress tends to increase enormously in the contact area of the mating teeth surface. The propagation of tooth damage causes instantaneous reduction in tooth stiffness. The vibration signal of gear transmission varies as the stiffness changes. Therefore the connection between surface fatigue failure, stiffness and vibration signal are of significant importance in monitoring gear defects. The purpose of this study is to conduct experimental investigations on the measurement of reduction in the gear teeth stiffness along with vibrations parameters. A pair of spur gears was tested under an accelerated test condition in an oil bath lubricated back-to-back gearbox. Experimental measurement of stiffness was carried out using modal analysis in conjunction with a theor...
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TL;DR: In this paper, a detailed literature review focuses on dynamics-based gearbox fault modeling, detection and diagnosis, focusing on the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, and gearbox transmission path modeling and method validation.
Abstract: Gearbox is widely used in industrial and military applications Due to high service load, harsh operating conditions or inevitable fatigue, faults may develop in gears If the gear faults cannot be detected early, the health will continue to degrade, perhaps causing heavy economic loss or even catastrophe Early fault detection and diagnosis allows properly scheduled shutdowns to prevent catastrophic failure and consequently result in a safer operation and higher cost reduction Recently, many studies have been done to develop gearbox dynamic models with faults aiming to understand gear fault generation mechanism and then develop effective fault detection and diagnosis methods This paper focuses on dynamics based gearbox fault modeling, detection and diagnosis State-of-art and challenges are reviewed and discussed This detailed literature review limits research results to the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, gearbox transmission path modeling and method validation In the end, a summary and some research prospects are presented
216 citations
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TL;DR: In this paper, an empirical mode decomposition (EMD) method for monitoring simulated faults using vibration and acoustic signals in a two-stage helical gearbox is described. And the results demonstrate the effectiveness of EMD based statistical parameters to diagnose severity of local faults on helical gears tooth.
Abstract: Gear is a vital transmission element, finding numerous applications in small, medium and large machinery. Excessive loads, speeds and improper operating conditions may cause defects on their bearing surfaces, thereby triggering abnormal vibrations in whole machine structures. This paper describes the implementation of empirical mode decomposition (EMD) method for monitoring simulated faults using vibration and acoustic signals in a two stage helical gearbox. By using EMD method, a complicated signal can be decomposed into a number of intrinsic mode functions (IMF) based on the local characteristic time scale of the signal. Vibration and acoustic signals are decomposed to extract higher order statistical parameters. Results demonstrate the effectiveness of EMD based statistical parameters to diagnose severity of local faults on helical gear tooth. Kurtosis values from EMD and that obtained from vibration and acoustic signals are compared to demonstrate the superiority of EMD based technique.
67 citations
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TL;DR: In this article , a digital twin-driven intelligent gear health management method is developed to assess the gear surface degradation progression, which can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life prediction and the predictive maintenance-based decision-making framework.
Abstract: • A digital twin-driven intelligent gear health management method is developed to assess the gear surface degradation progression. • A high-fidelity digital twin model is built for the gear transmission system. • A transfer learning algorithm is proposed for gear wear severity assessment. • Two endurance tests are conducted to validate the effectiveness of the proposed methodology. Gearbox has a compact structure, a stable transmission capability, and a high transmission efficiency. Thus, it is widely applied as a power transmission system in various applications, such as wind turbines, industrial machinery, aircraft, space vehicles, and land vehicles. The gearbox usually operates in harsh and non-stationary working environments, expediting the degradation process of the gear surface. The degradation process may lead to severe gear failures, such as tooth breakage and root crack, which could damage the gear transmission system. Therefore, it is essential to assess the progression of gear surface degradation in order to ensure a reliable operation. The digital twin is an emerging technology for machine health management. A high-fidelity digital twin model can help reflect the operation status of the gearbox and reveal the corresponding degradation mechanism, which could benefit the remaining useful life (RUL) prediction and the predictive maintenance-based decision-making framework. This paper develops a digital twin-driven intelligent health management method to monitor and assess the gear surface degradation progression. The developed method can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. Furthermore, the knowledge learned from digital twin models can be well transferred to the surface wear assessment of the physical gearbox in wide industrial applications, which is of great practical significance. Two endurance tests with different dominant degradation mechanisms were conducted to validate the effectiveness of the proposed methodology for gear wear assessment.
35 citations
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TL;DR: In this paper, the results of experimental investigations carried out to assess wear in spur gears of single stage spur gear box under fatigue test conditions were presented, which considered the lubricant film thickness analysis, wear mechanism studies on gear tooth surfaces, oil degradation analysis using Fourier transform infrared radiation (FTIR) method along with vibration signal analysis.
Abstract: Gears are one of the most common mechanisms for transmitting power and motion and their usage can be found in numerous applications. Studies on gear teeth contacts have been considered as one of the most complicated applications in tribology. The changes in operating conditions such as increase in temperature, load, reduction in viscosity result decrease in lubricant film thickness and degradation of lubricating oil thereby triggering several types of failures on tooth surfaces viz. pitting, scuffing, micro pitting, scoring, and spalling, these faults influence changes in vibration signals. This paper presents the results of experimental investigations carried out to assess wear in spur gears of single stage spur gear box under fatigue test conditions. The studies considered the lubricant film thickness analysis, wear mechanism studies on gear tooth surfaces, oil degradation analysis using Fourier transform infrared radiation (FTIR) method along with vibration signal analysis. The results provide a good understanding of tribological and vibration parameters as measures for effective assessment of wear in spur gears.
35 citations
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TL;DR: In this paper, a comprehensive dynamic model was developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication principles.
Abstract: Gear tooth surface wear is a common failure mode. It occurs over relatively long periods of service nonetheless, it degrades operating efficiency and leads to other major failures such as excessive tooth removal and catastrophic breakage. To develop accurate wear detection and diagnosis approaches at the early phase of the wear, this paper examines the gear dynamic responses from both experimental and numerical studies with increasing extents of wear on tooth contact surfaces. An experimental test facility comprising of a back-to-back two-stage helical gearbox arrangement was used in a run-to-failure test, in which variable sinusoidal and step increment loads along with variable speeds were applied and gear wear was allowed to progress naturally. A comprehensive dynamic model was also developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication (EHL) principles. The model consists of an 18 degree of freedom (DOF) vibration system, which includes the effects of the supporting bearings, driving motor and loading system. It also couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness and the excitation of different wear severities. Vibration signatures due to tooth wear severity and frictional excitations were acquired for the parameter determination and the validation of the model with the experimental results. The experimental test and numerical model results show clearly correlated behaviour, over different gear sizes and geometries. The spectral peaks at the meshing frequency components along with their sidebands were used to examine the response patterns due to wear. The paper concludes that the mesh vibration amplitudes of the second and third harmonics as well as the sideband components increase considerably with the extent of wear and hence these can be used as effective features for fault detection and diagnosis.
31 citations
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References
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TL;DR: A comprehensive survey of the studies involved in mathematical modelling of gears for dynamic analysis is made in this paper, where the basic characteristics of each class of dynamic models along with the objectives and different parameters considered in modeling are discussed.
Abstract: With increased demand for high speed machinery, the mathematical modelling of the dynamic analysis of gears has gained importance. Numerous mathematical models have been developed for different purposes in the past three decades. In this paper the mathematical models used in gear dynamics are discussed and a general classification of these models is made. First, the basic characteristics of each class of dynamic models along with the objectives and different parameters considered in modeling are discussed. Then, the early history of the research made on gear dynamics is summarized and a comprehensive survey of the studies involved in mathematical modelling of gears for dynamic analysis is made. Generally, a chronological order is followed in each class studied. The goal is not just to refer to several papers published in this field, but also to give brief information about the models and, sometimes, about the approximations and assumptions made. A considerable number of publications were reviewed and 188 of them are included in the survey.
573 citations
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TL;DR: In this paper, an experimental investigation on spur gears in which natural pitting was allowed to occur was conducted, and it was observed that based on the analysis of root mean square (rms) levels only the acoustic emission technique was more sensitive in detecting and monitoring pitting than either the vibration or spectrometric oil analysis (SOA) techniques.
Abstract: Prognosis of gear life using the acoustic emission (AE) technique is relatively new in condition monitoring of rotating machinery. This paper describes an experimental investigation on spur gears in which natural pitting was allowed to occur. Throughout the test period, AE, vibration and spectrometric oil samples were monitored continuously in order to correlate and compare these techniques to natural life degradation of the gears. It was observed that based on the analysis of root mean square (rms) levels only the AE technique was more sensitive in detecting and monitoring pitting than either the vibration or spectrometric oil analysis (SOA) techniques. It is concluded that as AE exhibited a direct relationship with pitting progression, it offers the opportunity for prognosis.
225 citations
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TL;DR: In this paper, a procedure for computing static transmission errors and tooth load sharing for low and high contact ratio internal and external spur gears was developed, and a suitable optimization algorithm was used to minimize any combination of the harmonics of gear mesh frequency components of static transmission error.
Abstract: A procedure for computing static transmission errors and tooth load sharing was developed for low and high contact ratio internal and external spur gears. A suitable optimization algorithm was used to minimize any combination of the harmonics of gear mesh frequency components of the static transmission error. Different combinations of tip and root relief may be used to achieve optimization. These include varying the starting point of relief and varying the magnitude of relief, and selecting the gear and/or the pinion teeth to be tip and/or root-relieved. Also, there exists an option for using either linear or parabolic relief. In addition to the presentation of optimal profile modifications, the effects of off-design loads, nonoptimum modifications, and random spacing errors are presented.
168 citations
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TL;DR: In this paper, the correlation of vibration analysis and wear debris analysis was investigated in an experimental test rig consisting of a worm gearbox driven by an electric motor, and the results from this paper have given more understanding on the dependent and independent roles of vibration and wear deformation analyses in machine condition monitoring and fault diagnosis.
Abstract: Vibration and wear debris analyses are the two main condition monitoring techniques for machinery maintenance and fault diagnosis. In practice, these two techniques are usually conducted independently, and can only diagnose about 30–40% of faults when used separately. However, recent evidence shows that combining these two techniques provides greater and more reliable information, thereby resulting in a more effective maintenance program with large cost benefits to industry.
In this paper, the correlation of vibration analysis and wear debris analysis was investigated. An experimental test rig consisting of a worm gearbox driven by an electric motor was set up to examine the correlation of the two techniques under various wear conditions. Three tests were conducted under the following conditions: (a) lack of proper lubrication, (b) normal operation, and (c) with the presence of contaminant particles added to the lubricating oil. Oil samples and vibration data were collected regularly. Wear debris analysis included the study of particle number and size distribution, the examination of particle morphology and types to determine possible wear mechanisms, and the analysis of chemical compositions to assess wear sources. Fault detection in the vibration signature was compared with the particle analysis. The results from this paper have given more understanding on the dependent and independent roles of vibration and wear debris analyses in machine condition monitoring and fault diagnosis.
128 citations
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01 Jan 1998
119 citations
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