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The purpose of this paper is to deal with the problem of integration of production and maintenance policies. In this context, the authors consider production systems made of parallel machines producing a single product over a finite horizon made of equal periods for which a forecasted demand is known. The authors investigate the impact of switching production in case of failure of any given machine.

A mathematical model is first developed to find an optimal production plan which minimizes the average total storage, shortage and production costs. Then, using this optimal production plan and taking into account the influence of the production rate on the degradation of each machine, optimal preventive maintenance (PM) policies are proposed for the situations with and without switching.

Optimal production rates are determined for each production period and for each machine. Optimal PM periods are also computed for each machine.

Usually, in manufacturing systems, the production rate of a machine influences its failure rate. In case a machine fails, it takes a random time to repair it during which production is lost. The paper attempts to propose a switching policy (SP) according to which the lost production is compensated by all the other machines. The effects of the SP coupled with the PM strategy are shown through a numerical example.

Contrarily to previous works, the authors consider more realistic settings with a non-negligible random time for repairing failed machines. In order to compensate the lost production during the repair of a failed machine, a SP is proposed to transfer the load uniformly to all the other machines. As a result, those machines will produce at a higher production rate and will consequently have their failure rate increased. It will therefore be essential to determine an optimal PM schedule knowing that durations of these activities are not negligible. It is shown that the simultaneous implementation of periodic PM and load transfer in case of failure is the most economical integrated strategy.

Reviews previous research on the links between business failures, macroeconomic conditions and insolvency law; and develops a mathematical, econometric model to investigate them further, using 1996‐1998 UK data. Presents and discusses the results, which suggest that the 1986 Insolvency Act did help to reduce the overall level of business failures and estimates that it saved 1100 companies from bankruptcy in the first three years after implementation. Finds that interest rates, price levels, levels of business formation, credit conditions and profit levels also affect business failure rates.

This study aims to focus on asset management of large‐scale information systems supporting infrastructures and especially seeks to address a methodology of their statistical deterioration prediction based on their historical inspection data. Information systems are composed of many devices. Deterioration process i.e. wear‐out failure generation process of those devices is formulated by a Weibull hazard model. Furthermore, in order to consider the heterogeneity of the hazard rate of each device, the random proportional Weibull hazard model, which expresses the heterogeneity of the hazard rate as random variables, is to be proposed.

Large‐scale information systems comprise many components, and different types of components might have different hazard rates. Therefore, when analyzing faults of information systems that comprise various types of devices and components, it is important to consider the heterogeneity of the hazard rates that exist between the different types of components. In this study, with this in consideration, the random proportional Weibull hazard model, whose heterogeneity of hazard rates is subject to a gamma distribution, is formulated and a methodology is proposed which estimates the failure rate of various components comprising an information system.

Through a case study using a traffic control system for expressways, the validity of the proposed model is empirically verified. Concretely, as for HDD, the service life at which the survival probability is 50 percent is estimated as 158 months. However, even for the same HDD, use environment differs according to usage. Actually, among the three different usages (PC, server, others), failures happen earliest in the case of PCs, which have the highest heterogeneity parameter and a survival probability of 50 percent after 135 months of usage. On the other hand, as for others, its survival probability is 50 percent at 303 months.

To operationally express the heterogeneity of failure rates, the Weibull hazard model is employed as a base, and a random proportional Weibull hazard model expressing the proportional heterogeneity of hazard rates with a standard gamma distribution is formulated. By estimating the parameter of the standard proportional Weibull hazard function and the parameter of the probability distribution that expresses the heterogeneity of the proportionality constant between the types, the random proportional Weibull hazard model can easily express the heterogeneity of the hazard rates between types and components.

The purpose of this paper is to analyze conditional failure rates, and prioritize water pipelines for replacement based on their expected failure rate where pipes are grouped based on age and pipe type. Thus, predictions can be made on the expected number of breaks in future years.

The time to failure of a pipe can be characterized by the stochastic properties of the population as a whole, from which the likelihood of component failure is derived. When the corresponding failure rate is plotted against time, a bathtub‐shaped curve results. The bathtub curve assists in determining maintenance schedules depending on the age of the pipe. Failure rates help determine whether the rates are more than an acceptable best practice threshold to signal replacement.

Ductile iron pipes had the highest failure rates, followed by asbestos cement pipes; PVC and concrete cylinder pipes had the lowest failure rates, but because concrete cylinder pipes are very time‐consuming to repair and very expensive to install, only PVC pipes are recommended on the basis of this study. Cast iron pipes fit the bathtub shape; ductile iron and asbestos concrete were somewhat bathtub shaped, though the early phase period was not apparent; the bathtub curve for concrete cylinder was fully inverted; while PVC pipes showed zero probability of failure during the middle period. The shapes of bathtub curves drawn on conditional failure rates were similar to those for the failure rates. The bathtub curves indicate that the general failure performance of pipe materials is somewhat contrary to general principles in manufacturing.

Analysis of failure serves a practical purpose for water utilities to allocate funds for pipe maintenance and prepare a schedule for pipe replacement, so as to provide the best quality services and safe drinking water to users of the utility.

The proper prioritization of water supply pipes for repair and replacement is of great social importance to the public at large, which expends considerable funds to maintain their drinking water supply.

The study of bathtub curves has not been seen before in the analysis of water supply pipes. A unique discovery is that the traditional shape of the bathtub curve is not always applicable for water supply pipes.

This study aims to obtained the failure probability distributions of subsystems for industrial robot and filtrate its fault data considering the complicated influencing factors of failure rate for industrial robot and numerous epistemic uncertainties.

A fault data screening method and failure rate prediction framework are proposed to investigate industrial robot. First, the failure rate model of the industrial robot with different subsystems is established and then the surrogate model is used to fit bathtub curve of the original industrial robot to obtain the early fault time point. Furthermore, the distribution parameters of the original industrial robot are solved by maximum-likelihood function. Second, the influencing factors of the new industrial robot are quantified, and the epistemic uncertainties are refined using interval analytic hierarchy process method to obtain the correction coefficient of the failure rate.

The failure rate and mean time between failure (MTBF) of predicted new industrial robot are obtained, and the MTBF of predicted new industrial robot is improved compared with that of the original industrial robot.

Failure data of industrial robots is the basis of this prediction method, but it cannot be used for new or similar products, which is the limitation of this method. At the same time, based on the series characteristics of the industrial robot, it is not suitable for parallel or series-parallel systems.

This investigation has important guiding significance to maintenance strategy and spare parts quantity of industrial robot. In addition, this study is of great help to engineers and of great significance to increase the service life and reliability of industrial robots.

This investigation can improve MTBF and extend the service life of industrial robots; furthermore, this method can be applied to predict other mechanical products.

This method can complete the process of fitting, screening and refitting the fault data of the industrial robot, which provides a theoretic basis for reliability growth of the predicted new industrial robot. This investigation has significance to maintenance strategy and spare parts quantity of the industrial robot. Moreover, this method can also be applied to the prediction of other mechanical products.

This study aims to identify factors influencing the bank failure rate in the USA over the period from 1970 to 2014 with an emphasis on economic/financial factors on the one hand and on banking legislation on the other hand. Regarding the latter, this study empirically investigates four major banking statutes: the Community Reinvestment Act of 1977; the Depository Institutions Deregulation and Monetary Control Act of 1980; the Federal Deposit Insurance Corporation Improvement Act of 1991; and the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. After adopting the technique of generalized method of moments (GMM), a robustness check in the form of autoregressive conditional heteroskedasticity (ARCH) is undertaken. Overall, the estimations imply that the bank failure rate was a decreasing function of the percentage growth rate of real gross domestic product (GDP) and the real interest rate yields on both three-month US Treasury bills and 30-year fixed-rate mortgages and an increasing function of the real cost of funds. In addition, there is strong evidence that the bank failure rate was increased by provisions in the Community Reinvestment Act of 1977 and the Depository Institutions Deregulation and Monetary Control Act of 1980, whereas the bank failure rate was decreased as a result of provisions in the Federal Deposit Insurance Corporation Improvement Act of 1991 and the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. Finally, there also is evidence that higher federal budget deficits elevated the bank failure rate.

After modeling the bank failure rate as a function of financial/economic variables and banking legislation, the times series from 1970 to 2014 is estimated by GMM and then by the ARCH techniques.

The results of the GMM and ARCH estimations imply that the bank failure rate in the US was a decreasing function of the percentage growth rate of real GDP as well as the real interest rate yields on both three-month US Treasury bills and 30-year fixed-rate mortgages and an increasing function of the real cost of funds. Furthermore, there is strong empirical support indicating that the bank failure rate was elevated by various provisions in the Community Reinvestment Act of 1977 and in the Depository Institutions Deregulation and Monetary Control Act of 1980, while the bank failure rate was reduced by certain provisions in the Federal Deposit Insurance Corporation Improvement Act of 1991 and the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994. There also is evidence that higher federal budget deficits increased the bank failure rate.

This study is the most contemporary (1970-2014) analysis of potential causes of the bank failure rate in the USA. The study also may be the first to apply the GMM and GARCH models to the problem. Also, some interesting policy implications are provided in the Conclusion.

Social scientists have recently turned their attention to the important consequences of industrial districts or so-called agglomeration economies on economic growth and firm performance. This paper explores an important but unanswered question involving agglomeration economies: does geographic location within an agglomeration affect firm performance? I assess this question by examining the effects of different geographic office locations (by zip code) on the failure rates of all corporate law firms located in Silicon Valley from 1969 to 1998. Empirical estimates reveal that Silicon Valley corporate law firms benefit from the increased volume of client referrals that comes from being near mutualistic firms that offer a different range of legal services, the lower labor costs and more specialized division of labor that come from being near a large joint supply of lawyers, and the increased business that comes from being near important clients (i.e. venture capital firms).

In addition, corporate law firms that locate in certain municipalities of Silicon Valley, including Palo Alto, San Jose, and Santa Clara, have significantly increased failure rates, even controlling for many firm-specific differences. Younger corporate law firms (under the age of 11 years) are helped disproportionately by being near important environmental resources and harmed disproportionately by being in certain perilous areas of Silicon Valley. All told, a law firm’s office location within Silicon Valley has significant consequences for its survival.

The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.

The paper presents an efficient methodology that was developed for the reliability prediction and the failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic.

The reliability prediction is based on the general features and characteristics of the MIL‐HDBK‐217FN2 technical document and a derating plan for the system design is developed in order to maintain low components’ failure rates. These failure rates are used in the FMECA, which uses fuzzy sets to represent the respective parameters. A fuzzy failure mode risk index is introduced that gives priority to the criticality of the components for the system operation, while a knowledge base is developed to identify the rules governing the fuzzy inputs and output. The fuzzy inference module is Mamdani type and uses the min‐max implication‐aggregation.

A typical power electronic device such as a switched mode power supply was analyzed and the appropriate reliability indices were estimated using the stress factors of the derating plan. The fuzzy failure mode risk indices were calculated and compared with the respective indices calculated by the conventional FMECA.

Further research efforts are needed for the application of fuzzy modeling techniques in the area of reliability assessment of electronic devices. These research efforts can be concentrated in certain applications that have practical value.

Practical applications can use a fuzzy FMECA modeling instead of the classical FMECA one, in order to obtain a more accurate analysis.

Fuzzy modeling of FMECA is described which can calculate fuzzy failure mode risk indices.

The aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.

In a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.

Findings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.

In failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.

In conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.