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An understanding of reliability physics, failure analysis and failure mechanisms as applied to semiconductor technologies is essential in assessing microcircuit reliability. The use of valid thermal accelerated tests and the correct use of the data therefrom is crucial in assessing reliability. Improved technical data are required from most manufacturers if user analysis of accelerated testing is to be more easily and accurately carried out.

Learning theory explains how organizations change as a result of experience, and can be used to predict the competitive strength of individual organizations and competitive pressures in organizational populations. We review extant learning theoretical propositions on how competitive strength is affected by experienced competition, founding conditions, and observed failures of other organizations. In addition, we propose that niche changes are an important source of learning. We test these propositions on data from the Norwegian general insurance industry. We find that historical density increases failure rates, contrary to some earlier findings, and also that the effect of founding density supports the density delay rather than trial-by-fire hypothesis. We find that failures of others before and during the lifetime of the organization reduce failure rates, and niche changes reduce failure rates for joint-stock companies but not for mutual firms. Overall the findings suggest that organizations learn more cheaply from the failures of others than from their own experiences, and that the stresses of competition can overwhelm the learning effects of competition.

A newly developed model for performing reliability and availability analysis of mechanical devices subject to multiple failure modes is presented. Using this model, reliability analyses of mechanical devices such as brakes, bearings, engines, fans, gears, generators, heat exchangers and pumps are developed. Real life failure rate data for these devices are obtained from various sources and are used in their reliability analyses. Principal failure modes of these devices are identified and expressions for reliability, state probabilities, mean time to failure and variance of time to failure are developed. For known field failure data reliability and state probability plots are shown.

The purpose of this study is to propose an extended reliability method for an industrial motor drive by integrating the physics of failure (PoF).

Industrial motor drive systems (IMDS) are currently expected to perform beyond the desired operating conditions to meet the demand. The PoF of the subsystem affects its reliability under such harsh operating circumstances. It is crucial to estimate reliability by integrating PoF, which helps in understanding its impact and to develop a fault-tolerant design, particularly in such an integrated drive system. An integrated PoF extended reliability method for industrial drive system is proposed to address this issue. In research, the numerical failure rate of each component of industrial drive is obtained first with the help of the MIL-HDBK-217 military handbook. Furthermore, the mathematically deduced proposed approach is modeled in the GoldSim Monte Carlo reliability workbench.

From the results, for a 15% rise in integrated PoF, the reliability and availability of the entire IMDS dropped by 23%, resulting in an impact on mean time to failure (MTTF).

The integrated PoF of the motor and motor controller affects industrial drive reliability, which falls to 0.18 with the least MTTF (2.27 years); whose overall reliability of industrial drive drops to 0.06 if it is additionally integrated with communication protocol.

The purpose of this paper is to analyse the influence of the institutional environment on entrepreneurial failure of certain characteristics, both formal (regulatory complexity and tax pressure) and informal (social capital and fear of failure).

The authors use data drawn from a panel of 37 countries over a period of nine years (2006-2014).

Results show that the greater the regulatory complexity, the higher the rate of entrepreneurial failure; also that the higher the country’s stock of social capital, the lower the rate of entrepreneurial failure. Finally, the greater the tax pressure, the lower the rate of business failure.

Among the limitations of this paper is the difficulty of directly measuring the variables it analyses, making it necessary to use proxies.

This study has important practical implications for policymakers. First, the study provides important insights on how regulatory complexity positively affects entrepreneurial failure. In other words, the study represents a response to the call for the development of a better regulatory environment since this plays a significant role in entrepreneurial failure. Second, regarding tax pressure, the authors found that the greater the tax pressure, the lower the rate of entrepreneurial failure. In this respect, entrepreneurs, academics and policymakers should be aware of this result. Finally, this study also demonstrates the important role of social capital in preventing entrepreneurial failure.

In line with the findings, this study provides proof of how the institutional framework can have an influence on entrepreneurial failure.

In order to obtain a correct failure diagnosis and to prepare more effective maintenance programmes, it is essential to have reliable forecasts about the trend of equipment and machinery failure rate. In this work attention is focused on the transport sector. First the strong points of the “intelligent” techniques of forecasting are analysed compared to the more traditionally used statistical methodologies, and then a forecasting neural model of the failure rate of the equipment installed on buses and trains is proposed. Finally an application of the model is presented.

For manufacturing systems which are in continuous operation and subject to breakdown, inspection can be an appropriate maintenance strategy. In this situation, inspection can reduce down‐time and increase system reliability. In this paper two main ideas are proposed. In the first, an inspection effect function is introduced which modifies the traditional system failure rate distribution. This modification involves a formula which demonstrates the effect of inspection frequency and inspection effectiveness on system failure rate distribution. It is then argued that under inspection policy the system’s traditional failure rate is necessarily affected by these factors. The second idea presents a maintenance model in which the system is interrupted in its time to failure by inspections. Optimisation of this model determines an optimal inspection frequency which minimises the system’s total down‐time. Thus, it is shown that by optimising inspection frequency system availability can be increased.

The traditional maintenance scheduling strategies of multi-component systems may result in maintenance shortage or overage, while system degradation information is often ignored. The purpose of this paper is to propose a multi-phase model that better integrates degradation information, dependencies and maintenance at the tactical level.

This paper proposes first a maintenance optimization model for multi-component systems with economic dependence and structural dependence. The cost of combining maintenance activities is lower than that of performing maintenance on components separately, and the downtime cost can be reduced by considering structural dependence. Degradation information and multiple maintenance actions within scheduling horizon are considered. Moreover, the maintenance resources can be integrated into the optimization model. Then, the optimization model adopting one maintenance activity is extended to multi-phase optimization model of the whole system lifetime by taking into account the cost and the expected number of downtime.

The superiority of the proposed method compared with periodic maintenance is demonstrated. Thus, the values of both integrated degradation information and considering dependencies are testified. The advantage of the proposed method is highlighted in the cases of high system utilization, long maintenance durations and low maintenance costs.

Few studies have been carried out to integrate decisions on degradation, dependencies and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic multi-phase model is proposed in this paper, along with an iterative solution algorithm for it.

The purpose of this paper is to present age-based replacement models subject to shocks and failure rate in order to determine the optimal replacement cycle. As a result, according to system reliability, maintenance costs of the system are to be minimized.

First, the modeling with respect to assumptions and two major factors (shocks and failure rate) is done. Second, by using of MATLAB the optimal parameters are obtained. Finally, analysis of results and comparison of models are done.

Analysis of results shows all models provide optimal replacement cycle and at this time, cost rate of the system by considering the reliability rate is minimal. Also with an increase of one unit to two units, reliability rate increases much higher than the rate of cost.

This work provides models that in addition to considering the failure rate (internal factors), also shocks as an external factor have been considered. By considering these two factors more comprehensive and adaptable models have been proposed.

The purpose of this paper is to deal with the formation of performance modeling and maintenance priorities for the water flow system (WFS) of a coal-based thermal power plant.

The system consists of five subsystems, i.e. condenser, condensate extraction pump, Low Pressure Heater, deaerator and boiler feed pump. The Chapman-Kolmogorov equations are generated on the basis of transition diagram and further solved recursively to obtain the performance modeling with the help of normalizing condition using Markov approach.

Availability matrices are formed with the help of different combinations of failures and repair rates of all subsystems. The performance of all subsystems is evaluated in terms of availability level achieved in availability matrices and plots of failure rates and repair rates of various subsystems. The maintenance priorities of various subsystems of WFS are decided on the basis of repair rate.

The adoption of both performance modeling and maintenance priorities decision by the management of thermal power plant will result in the enhancement of system availability and reduction in maintenance cost.