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Summarizes briefly the dramatic advances made in the reliability of mechanical seals for rotating shafts in the process chemical and petrochemical industries over the last 30 years. Shows that expected mean time before failure has improved from tens of days to years over that time.

The purpose of this paper is contribution to estimate the potential of wind energy in Douala in Cameroon, by modeling and predicting the regime of wind. The paper deals with the analysis and comparison of seven numerical methods for the assessment of effectiveness in determining the parameters for the Weibull distribution, using wind speed data collected at Douala International Airport in Cameroon, in the period from September 2011 to May 2013, obtained by meteorological equipment belonging to the Laboratory of Energy Research of the Institute of Geological and Mining Research.

By using ANOVA, root mean square error and chi-square tests to compare the proposed methods, this study aims to determine which methods are effective in determining the parameters of the Weibull distribution for the available data, in an attempt to establish acceptable criteria for better usage of wind power in Douala, which is the economic capital and ought to have prominence in the use of renewable sources for electricity generation in Cameroon.

The study helps to determine that moment, empirical and energy pattern factor methods used to determine the shape parameter k and the scale parameter c of the Weibull distribution present a better curve fit with the histogram of the wind speed. This fact is clearly validated by means of the statistical tests. But, all the seven methods gave excellent performance. Then, k reaching levels ranging from 3.5 to 5.5 and c range from 1.7 to 2.4.

Then as far as we are concerned, for a significant contribution, it could be more effective to have a model for prediction of wind characteristics using wind data collected per hour, one at least three years. A comparison of results obtained from lots of other methods (seven in this case) is necessary before an efficient discussion. Standard deviations and errors between measured and predicted data must also be presented.

The purpose of this paper is to propose a statistical approach, which enables the prediction of the risk of the appearance of discharge bearing currents in a given drive that can damage the motor bearings. Using such an approach, the drives for the decisive but expensive, long‐term destructive investigations can be selected. A comparative analysis is presented to show the applicability of the presented statistical model.

In the paper, a statistical approach to large data sets of measured bearing currents is presented. Weibull distribution has been chosen to describe the amplitudes of bearing currents, whereas exponential distribution is used for the approximation of the awaiting times to puncture. In order to check the hypothesis that these distributions are independent two tests has been conducted: F‐Snedecor, to check equality of mean values and Bartlett's to check variance equality. On this basis, joint distribution is expressed as the product of independent marginal distributions.

The comparative analyses show that special caution is required for a proper selection of EMI filters. The additional inductance of the filter in a common mode (CM) current path decreases the damping factor of the circuit and significantly increases the risk of bearing damage caused by electric discharge machining currents. The CM transformer is a cheap and effective solution that reduces CM current without increasing the rate and the amplitudes of bearing currents.

This paper presents a new approach to bearing currents that allows prediction of the risk of bearing damage in a given drive.

Load haul dumper (LHD) is one of the main ore transporting machineries used in underground mining industry. Reliability of LHD is very significant to achieve the expected targets of production. The performance of the equipment should be maintained at its highest level to fulfill the targets. This can be accomplished only by reducing the sudden breakdowns of component/subsystems in a complex system. The identification of defective component/subsystems can be possible by performing the downtime analysis. Hence, it is very important to develop the proper maintenance strategies for replacement or repair actions of the defective ones. Suitable maintenance management actions improve the performance of the equipment. This paper aims to discuss this issue.

Reliability analysis (renewal approach) has been used to analyze the performance of LHD machine. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the maximum likelihood estimate (MLE) method.

Independent and identical distribution (IID) assumption of data sets was validated through trend and serial correlation tests. On the basis of test results, the data sets are in accordance with IID assumption. Therefore, renewal process approach has been utilized for further investigation. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the MLE method. Reliability of each individual subsystem has been computed according to the best-fit distribution. In respect of obtained reliability results, the reliability-based preventive maintenance (PM) time schedules were calculated for the expected 90 percent reliability level.

As the reliability analysis is one of the complex techniques, it requires strategic decision making knowledge for the selection of methodology to be used. As the present case study was from a public sector company, operating under financial constraints the conclusions/findings may not be universally applicable.

The present study throws light on this equipment that need a tailored maintenance schedule, partly due to the peculiar mining conditions, under which they operate. This study mainly focuses on estimating the performance of four numbers of well-mechanized LHD systems with reliability, availability and maintainability (RAM) modeling. Based on the drawn results, reasons for performance drop of each machine were identified. Suitable recommendations were suggested for the enhancement of performance of capital intensive production equipment. As the maintenance management is only the means for performance improvement of the machinery, PM time intervals were estimated with respect to the expected rate of reliability level.

Roller compacted concrete (RCC) pavement is used in areas subjected to heavy impact loads; therefore, higher impact resistance is a desirable property of consideration. This study aims to investigate the effect of partial replacement of fine aggregate with crumb rubber (CR) and the addition of nanosilica (NS) by weight of cementitious materials on the impact resistance of roller compacted rubbercrete (RCR).

Four replacement levels of CR (0, 10, 20 and 30 per cent) and four addition levels of NS (0, 1, 2 and 3 per cent) were considered. The impact resistance test was carried out using the drop weight test recommended by ACI 544.

The results showed that the impact resistance of RCR increases with an increase in both CR and NS addition, though for CR above 20 per cent, sudden drop in impact resistance was observed. However, NS reduces the ductility of RCR by decreasing the post-cracking impact resistance. Response surface methodology was used to develop models for predicting the impact resistance of RCR, and the developed models showed a high degree of correlation. As a result of wide variations in the impact drop test data, two-parameter Weibull distribution function was used for the data analysis, and it was found that the probabilistic distributions of the first crack and ultimate failure impact resistance follow the two-parameter Weibull distribution function.

In this work, the effect of partial replacement of fine aggregate with CR and the addition of NS by weight of cementitious materials on the impact resistance of RCC pavement has been investigated. CR has been used to increase the impact resistance of RCC Pavement.

India's textile industries play a vital role in the Indian economy. These industries consume the highest thermal energy (steam power). The demand of the steam in process industries is increasing rapidly, and this demand can be met by increasing the capacity utilization of steam boilers. The purpose of this paper is to present a new approach for reliability analysis by expert judgment method.

A lack of adequate life data is one of the biggest challenge in the reliability analysis of mechanical systems. This research provides an expert judgment approach for assessing the boiler's reliability characteristics. For this purpose, opinions of experts on time to failure and time to repair data were elicited in the form of statistical distributions. In this work, reliability analysis of the boiler system is carried out by expert judgment method and by using best-fit failure model. The system reliability along with preventive maintenance intervals of all components is also evaluated.

It is observed that the reliability analysis results obtained by expert judgment method and best-fit failure model method indicate that there are no significant differences. Therefore, in case when insufficient data are available, the expert judgment method can be effectively used. The analysis shows that the feedwater tank, feedwater pump, supply water temperature sensor, strainer, return water temperature sensor, condensate filter, mechanical dust collector, coal crusher and fusible plug are identified as critical components from a reliability perspective, and preventive maintenance strategy is suggested for these components.

In this research paper, a system reliability model by the expert judgment method is developed, and it can be effectively used where insufficient failure data are available. This paper is useful for the comparative evaluation of reliability characteristics of a boiler system by expert judgment method and best-fit failure model method.

Extreme high temperatures are a significant feature of global climate change and have become more frequent and intense in recent years. These pose a significant threat to both human health and economic activity, and thus are receiving increasing research attention. Understanding the hazards posed by extreme high temperatures are important for selecting intervention measures targeted at reducing socioeconomic and environmental damage.

In this study, detrended fluctuation analysis is used to identify extreme high-temperature events, based on homogenized daily minimum and maximum temperatures from nine meteorological stations in a major grassland region, Hulunbuir, China, over the past 56 years.

Compared with the commonly used functions, Weibull distribution has been selected to simulate extreme high-temperature scenarios. It has been found that there was an increasing trend of extreme high temperature, and in addition, the probability of its indices increased significantly, with regional differences. The extreme high temperatures in four return periods exhibited an extreme low hazard in the central region of Hulunbuir, and increased from the center to the periphery. With the increased length of the return period, the area of high hazard and extreme high hazard increased. Topography and anomalous atmospheric circulation patterns may be the main factors influencing the occurrence of extreme high temperatures.

These results may contribute to a better insight in the hazard of extreme high temperatures, and facilitate the development of appropriate adaptation and mitigation strategies to cope with the adverse effects.

The purpose of this paper is to provide a new method to estimate the reliability of series system by using copula functions. This problem is of great interest in industrial and engineering applications.

The authors introduce copula functions and consider a Bayesian analysis for the proposed models with application to the simulated data.

The use of copula functions for modeling the bivariate distribution could be a good alternative to estimate the reliability of a two components series system. From the results of this study, the authors observe that they get accurate Bayesian inferences for the reliability function considering large samples sizes. The Bayesian parametric models proposed also allow the assessment of system reliability for multicomponent systems simultaneously.

Usually, the studies of systems reliability engineering assume independence among the component lifetimes. In the approach the authors consider a dependence structure. Using standard classical inference methods based on asymptotical normality of the maximum likelihood estimators for the parameters the authors could have great computational difficulties and possibly, not accurate inference results, which there is not found in the approach.

Reliability analysis is required to identify the components or subsystems with low reliability for a given designed performance. Life cycle cost analysis helps understand the cost implications over the entire life span of a product. The purpose of this paper is to present a case study describing reliability analysis and life cycle cost optimization of a band saw cutting machine manufactured and used in India.

The data required for reliability analysis is collected from the manufacturer and users of band saw cutting machine. The parameters of failure distribution have been estimated by using ReliaSoft’s Weibull++6 software. The life cycle cost is divided into various cost elements such as acquisition cost, operation cost, failure cost, support cost and net salvage value.

The results of the analysis show that the components such as band wheel bearing, guide roller bearing, limit switch, carbide pad, hydraulic cylinder oil seal, control panel dial, control panel and solenoid valve are critical from reliability and life cycle cost analysis perspective.

With certain design changes it is found that the reliability of the system is increased by 15.85 percent while the life cycle cost is reduced by 22.09 percent. The study also shows that the reliability analysis is useful for deciding maintenance intervals.

Recently, a new class of fatigue crack growth models based on elastoplastic stress‐strain histories at the crack tip region and strain‐life fatigue damage models have been proposed. The fatigue crack propagation is understood as a process of continuous crack initializations, over elementary material blocks, which may be governed by strain‐life data of the plain material. The residual stresses developed at the crack tip play a central role in these models, since they are used to assess the actual crack driving force, taking into account mean stresses and loading sequential effects. The UniGrow model fits this particular class of fatigue crack propagation models. The purpose of this paper is to propose an extension of the UniGrow model to derive probabilistic fatigue crack propagation data, in particular the derivation of the P–da/dN–ΔK–R fields.

An existing deterministic fatigue crack propagation model, based on local strain‐life data is first assessed. In particular, an alternative methodology for residual stress computation is proposed, based on elastoplastic finite element analysis, in order to overcome inconsistencies found in the analytical approximate approaches often used in literature. Then, using probabilistic strain‐life fields, a probabilistic output for the fatigue crack propagation growth rates is generated. A new probabilistic fatigue field is also proposed to take mean stress effects into account, using the Smith‐Watson‐Topper (SWT) damage parameter. The proposed models are assessed using experimental data available for two materials representative from old Portuguese bridges.

A new method to generate probabilistic fatigue crack propagation rates (P–da/dN–ΔK–R fields) is proposed and verified using puddle iron from old Portuguese bridges, usually characterized by significant scatter in fatigue properties. Also, a new probabilistic fatigue field for plain material is proposed to deal with mean stress effects.

A relation between the P–ε–N and the P–da/dN–ΔK–R fields is firstly proposed in this research. Furthermore, a new P–SWT–N field is proposed to deal with mean stress effects.