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In this study, we employed a geometric process approach to resolve gearbox maintenance problems. The approach is realistic and direct in modelling the characteristics of a deteriorating system such as a gearbox since a decreasing geometric process can model a gearbox’s successive operating times and an increasing geometric process can model the corresponding consecutive repair times. First, two test statistics were used to check whether the process was geometric or not. Next, model parameters of the geometric process were estimated using the simple linear regression techniques. Finally, the optimal replacement policy based on minimising the long‐run average cost per day was determined for each type of gearbox.

The purpose of this paper was to perform an experimental investigation to analyze vibration and noise of unloaded gearbox with different oil quality. All motor-driven machinery used in the modern world can develop faults. The maintenance plans include analyzing the external relevant information of critical components, in order to evaluate its internal state. From the beginning of the twentieth century, different technologies have been used to process signals of dynamical systems.

A proposed neural network (NN) is also employed to predict vibration parameters of the experimental test rig. Moreover, four types of oils are used for gearbox to predict reliable oil. Vibration signals extracted from rotating parts of machineries carry lot many information within them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or the assembly under study. The experimental stand for testing an unloaded gearbox is composed by actuated direct current (DC) driving system.

This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gearbox using two types of artificial neural networks (ANNs) and stress analyzed with computer-based software ANNs. The results improved that the proposed NN has superior performance to adapt experimental results.

This paper is one such attempt to apply machine learning methods like ANN. This work deals with extraction of wavelet features from the vibration data of a gearbox system and classification of gear faults using ANNs.

These kind of NN-based approaches are novel approaches to predict real-time vibration and acceleration parameters of unloaded gearbox with five types of oils. Also, the investigation contains new information about studied process, containing elements of novelty.

The purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.

Vibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.

The obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.

The proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.

In this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Wind energy is one of the most promising and the fastest growing alternative-energy production technologies, which have been developed in response to stricter environmental regulations, the depletion of fossil-fuel reserves, and the world's ever-growing energy needs. This form of alternative energy is projected to provide 20 percent of the US energy needs by 2030. For economic reasons, wind turbines (articulated structures that convert wind energy into electrical energy) are expected to operate, with only regular maintenance, for at least 20 years. However, some key wind turbine components (especially the gearbox) tend to wear down, malfunction and fail in a significantly shorter time, often three to five years after installation, causing an increase in the wind-energy cost and in the cost of ownership of the wind turbine. Clearly, to overcome this problem, a significant increase in long-term gearbox reliability needs to be achieved.

While purely empirical efforts aimed at identifying shortcomings in the current design of the gearboxes are of critical importance, the present work demonstrates that the use of advanced computational engineering analyses, like the finite-element stress analysis and a post-processing fatigue-life assessment analysis, can also be highly beneficial.

The results obtained in the present work clearly revealed how a variety of normal operating and extreme wind-loading conditions can influence the service-life of a wind-turbine gearbox in the case when the service-life is controlled by the gear-tooth bending-fatigue.

The present work attempts to make a contribution to the resolution of an important problem related to premature-failure and inferior reliability of wind-turbine gearboxes.

This study aims to numerically investigate the multi-phase flow and thermal physics inside gearboxes, which is critical to the theoretical analysis of energy transfer.

To explore the churning power losses, a three-dimensional numerical model of the gearbox is built using the RNG k–e turbulence model and three alternative moving mesh strategies (i.e. the dynamic mesh [DM], sliding mesh and immersion solid methods). The influence of the rotational speed on the transient flow field, including the oil distribution, velocity and pressure distribution and the churning losses, is obtained. Finally, the time-dependent thermo-fluid state of the gearbox is predicted.

The findings show that the global DM method is preferable for determining the flow structures and power losses. The rotational speed exerts a significant effect on the oil flow and the wheel accounts for most of the churning losses. Based on the instantaneous temperature distribution, the asymmetric configuration leads to the initial bias of the high-temperature region towards the pinion. Additionally, the heat convection efficiency of the tooth tip is slightly higher than that of the tooth root.

An in-depth understanding of the flow dynamics inside the gearbox is essential for its optimisation to decrease the power and enhance heat dissipation during operation.

This paper aims to analyze fatigue failures of a typical marine gearbox under harsh ocean conditions, and these failures are reasonably attributed to the bearing fretting fatigue damages.

Two typical FAG cylindrical roller bearings mounted on this marine gearbox are particularly used for analysis, as they are most vulnerable to these failures. A series of simulations have also been conducted to verify the analysis results and failure reasons by reproducing the fretting fatigue damages for the same shaft-bearing system under the same manufacturing error conditions.

The analysis results indicate that manufacturing errors are the most possible reasons for the bearing failures, and these errors have more effects on the FAG cylindrical roller bearing as compared to other bearings mounted on the same shaft system. The simulations results are in good agreement with the theoretical analysis results and test results and hence validate that manufacturing errors are the dominant reasons for bearing fretting fatigue damages in this typical marine gearbox.

Fatigue failures of a typical marine gearbox. Manufacturing errors are the most possible reasons for the bearing failures. A series of simulations have been conducted to verify the analysis results and failure reasons. The simulations results are in good agreement with the theoretical analysis results and test results.

Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar.

CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI).

The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers.

The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

This paper aims to investigate the effect of misalignment on wear of spur gears and on oil degradation using online sensors.

The misalignment effect on gears is created through a self-alignment bearing, and is measured using laser alignment system. Several online sensors such as Fe-concentration sensor, moisture sensor, oil condition sensor, oil temperature sensor and metallic particle sensor are installed in the gear test rig to monitor lubricant quality and wear debris in real time to assess gearbox failure.

Offset and angular misalignments are detected in both vertical and horizontal planes. The failure of misaligned gear is observed at both the ends and on both the surfaces of the gear teeth. Larger-size ferrous and non-ferrous particles are traced by metallic particle sensor due to gear and seal wear caused by misalignment. Scanning electron microscope (SEM) images examine chuck, spherical and flat platelet particles, and confirm the presence of fatigue (pitting) and adhesion (scuffing) wear mechanism. Energy-dispersive X-ray spectroscopy analysis of SEM particles traces carbon (C) and iron (Fe) elements due to gear failure.

Gear misalignment is one of the major causes of gearbox failure and the lubricant analysis is as important as wear debris analysis. A reliable online gearbox condition monitoring system is developed by integrating wear and oil analyses for misaligned spur gear pair in contact.

Besides other approaches, fuel savings in automotive applications and energy savings, in general, also require high‐efficiency gearboxes. Different approaches are shown regarding how to further improve gearbox efficiency. This paper aims to address these issues.

The paper takes the following approach: theoretical and experimental investigations of bearing arrangements and gear design as well as lubricant type and lubricant supply to the components lead to efficiency optimisation.

No‐load losses can be reduced, especially at low temperatures and part‐load conditions when using low‐viscosity oils with a high viscosity index and low oil immersion depth or low spray oil supply of the components. Bearing systems can be optimised when using more efficient systems than cross‐loading arrangements with high preload. Low‐loss gears can contribute substantially to load‐dependent power loss reduction in the gear mesh. Low‐friction oils are available for further reduction of gear and bearing mesh losses. All in all, a reduction of the gearbox losses in an average of 50 per cent is technically feasible.

Results from different projects of the authors and from the literature are combined to quantitatively evaluate the potential of power loss reduction in gearboxes.