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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.

Uses the technique of delay‐time analysis to model preventive maintenance problems of gearboxes for the Kowloon Motor Bus (KMB) Company Limited in Hong Kong. Presents a basic model of inspection maintenance, along with models of some of the variations which have been observed in the case being studied. Aims, in particular to identify the problems and requirements of KMB and then develop a delay‐time model to fit the case, as well as to determine a preventive maintenance policy derived from the model so that the total maintenance cost per unit time can be minimized. Verifies the feasibility of implementing the policy according to three criteria: workers’ utilization, downtime of the gearboxes and the monthly cost incurred by both maintenance and overhead. Compares the results with those of the current policy. Suggests recommendations to KMB for improving the current maintenance policy, and highlights the general applicability of the delay‐time modelling technique.

The purpose of this paper is to recognize and label the faults in wind turbines with a new density-based clustering algorithm, named contour density scanning clustering (CDSC) algorithm.

The algorithm includes four components: (1) computation of neighborhood density, (2) selection of core and noise data, (3) scanning core data and (4) updating clusters. The proposed algorithm considers the relationship between neighborhood data points according to a contour density scanning strategy.

The first experiment is conducted with artificial data to validate that the proposed CDSC algorithm is suitable for handling data points with arbitrary shapes. The second experiment with industrial gearbox vibration data is carried out to demonstrate that the time complexity and accuracy of the proposed CDSC algorithm in comparison with other conventional clustering algorithms, including k-means, density-based spatial clustering of applications with noise, density peaking clustering, neighborhood grid clustering, support vector clustering, random forest, core fusion-based density peak clustering, AdaBoost and extreme gradient boosting. The third experiment is conducted with an industrial bearing vibration data set to highlight that the CDSC algorithm can automatically track the emerging fault patterns of bearing in wind turbines over time.

Data points with different densities are clustered using three strategies: direct density reachability, density reachability and density connectivity. A contours density scanning strategy is proposed to determine whether the data points with the same density belong to one cluster. The proposed CDSC algorithm achieves automatically clustering, which means that the trends of the fault pattern could be tracked.

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 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.

The non-stationary operational wind loads vary in time and site and has remarkable effect on wind turbine drive train. The purpose of this paper is to determine the effects of wind class 3 and 7 on the life of wind turbine drive train. The two-wind class 3 and 7 are described by average wind speed and weight factor and effects of two variables on wind energy generation and wind turbine drive train studied.

The load distribution method is used to calculate stress range cycles for wind class 3 and 7. To determine the rise of force on wind turbine drive train, the load cycle method is proposed. The fatigue damage model is studied with respect to influence of different wind speeds and wind shear factor and then results analysed accordingly. Also sensitivity analysis has been carried out to assess the percentage of drop of energy generation and rise of tangential force for wind class 3 and 7. Linear fit method is used to determine the inclination of wind variation and wind shear of wind class 3 and 7. In this regard, two practical wind sites fall under the wind class 3 and 7 and 1.5 MW wind turbine have been taken in to account.

The results showed that the average rise of force on wind turbine drive train is 37.5% which can influence the drop in energy 34.7% for wind class 3. Similarly, the results of wind class 7 are showing that the average rise in force and drop in energy found to be 49.05% and 51.16%, respectively. The wind class 7 have higher tendency of wind fluctuations and weight factor that can cause a damage to wind turbine drive train components. The results showed that when wind speed increases to rated power 11.5 m/s the damages occurred and remain steady. Similarly, when weight factor increased from 0.18 to onwards the damage occurred. The increased wind loads increased the tangential loads on the wind turbine decreased life of the gearbox.

The results of study suggest that wind turbine should be design according to site specific wind environment for maximum energy generation and lowers the wind loads on the drive train component.

Resilience as a novel concept has attracted the most attention in the management of engineering systems. The main goal of engineering systems is production assurance and increasing customer satisfaction which depends on the suitable performance of mechanical equipment. “A resilient system is defined as a system that is resistant to disruption and failures and can recover itself and returns to the state before failure as soon as possible in the case of failure.” Estimate the value of the system’s resilience to increase its resilience by covering the weakness in the resilience indexes of the system.

In this article, a suitable approach to estimating resilience in complex engineering systems management in the field of mining has been presented. Accordingly, indexes of reliability, maintainability, supportability, efficiency index of prognostics and health management of the system, and ultimately the organization resilience index, have been used to evaluate the system resilience.

The results of applying this approach indicate the value of 80% resilience if the risk factor is considered and 98% if the mentioned factors are ignored. Also, the value of 58% resilience of this organization’s management group indicates the weakness of situational awareness and weakness in the vulnerable points of the organization.

To evaluate the resilience in this article, five indicators of reliability, maintainability, and supportability are used as performance indicators. Also, organization resilience and the prognostic and health management of the system (PHM) are used as management indicators. To achieve more favorable results, the environmental and operational variables governing the system have been used in performance indicators, and expert experts' opinions have been used in management indicators.

The purpose of this paper is to suggest new method for improving the condition indicators (CIs) used in health and usage monitoring system based on signal separation of gears.

The research method is based on employing signal separation techniques to improve gears signal and fault signature. The signal separation is based on adaptive filters concept.

CIs estimated for the deterministic part of vibration signal show higher sensitivity to gears faults in comparison to indicators estimated based on the original signal. This method proposed could enhance early fault detection in gears, particularly for those applications where strong background noise from other sources in the machine masks the characteristics fault components.

The contribution of this research is improving the CIs currently used for helicopter gearboxes. As consequence the safe operation and availability will be improved.

This paper aims to argue for a China transmuting to fast overtake USA in innovation based on the extrapolation of past statistical trend. Case studies in self and co-innovation are provided so that the documentation of the dynamics of knowledge flows and a brain-linguistic explanation is given as to why, in the future, the Chinese are likely to lead in innovation.

This paper illustrates a multi-method approach in research for Chinese Management Studies. First, the sociological background of China is highlighted (Mao Zedong’s aphorism). Second, insights from OECD patent database are utilized. Third, the use of comparative research and development case-studies: self-innovation (Chinese) and co-innovation (contrasting Japanese versus French cooperation with Chinese). Fourth, is the inter-disciplinarily approach wherein the assimilating of knowledge is related to recent advances in brain research. Fifth, emphasizing the different levels in organizing for innovation: national (China), organizational (SOE), group processes and person-to-person, synapses within individual brains.

Statistical trend suggests that China is transforming and is on the path toward overtaking the USA in innovation. When will this happen? Using extrapolation as an indication, China may surpass the USA by the 50 per cent mark within the next decade. Insights into the processes of self-innovation and co-innovation are provided. Authors argue for a brain-linguistic explanation (Hebb, 1949) for further understanding why China will eventually lead ceteris paribus innovation, a function of the human brain.

This paper highlights on the basis of statistical trends (using OECD database) a rising, innovative China that is poised to overtake the USA in the near future. A major contribution is in providing insights of interactional processes required to foster innovation: self and co-innovation (comparing Japanese and French). The critical brain-linguistic role as the rationale as to why the Chinese are given a greater, more developed brain power that will eventually surpass the West in innovation.