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The purpose of this analysis was to attempt to improve the reliability of a rotor support system of a modern aircraft engine.

The process used for carrying out FMEA is specified by MIL‐STD‐1629A procedure for carrying out failure mode, effects and criticality analysis.

In increasing demand in the avionics sector, particularly in modern defence and civil aircraft, safety and reliability are the prime concerns to complete the mission successfully. Technocrats are made to rethink the safety of complete systems by adding redundancy to the critical activities. A rotor support system (RSS) is an integral part of a gas turbine engine used in any aircraft. As its name implies, the rotor support system shares the load of the rotating component of an engine, hence the rotor support system plays a vital role in any aircraft engine. It shares the load of compressor rotor and stator, turbine rotor and stator, inter‐casing, and exhaust system of a gas turbine engine. Any failure in such a system may make the entire aircraft fail. Therefore it is worth carrying out Failure Modes and Effects Analysis (FMEA) on such a critical system. FMEA is one of the effective reliability assessment tools, which evaluate systematically and document the potential failure modes of a system or equipment and their causes. It helps in grading the severity of all potential failure modes and is useful in carrying out the changes in the early phase of design. The analysis starts with the potential failure of the smallest component at the final indenture and goes up to the initial indenture level.

The paper adds insight into the reliability improvement of the rotor support system of modern aircraft.

The purpose of this paper is to evaluate the performance of paper mill plant, which is a very key factor in improving its production. A number of safety challenges can be successfully fixed for a paper mill to continue to make further improvements in reliability, safety and economics. Many incidents in the paper mill plants are frequently caused by human error and equipment failure. Many of the incidents are generally based on poor managerial strategies. These types of errors could have been prevented if safety instructions had been correctly followed and supported in the maintenance system.

A paper mill plant mainly consists four sections namely the head box, wire part, press part and dryer. All these four parts are connected in series configuration. The authors have developed a mathematical model for the plant in which power supply is in standby mode. The designed system can fail in five ways, i.e. by the failure of head box failure, wire part failure, press part failure, dryer failure and power (electricity) failure. So to make proper functioning of paper machine and no interruption in power supply, the authors have considered that power supply is in standby mode.

Using the supplementary variable technique, the Laplace transformations and Markov process theory, the reliability indices of the paper mill plant model are determined.

In the present paper, the authors have developed a mathematical model based on a paper plant machine.

Describes research into the problem of estimating the distribution parameters of failure data of industrial equipment subject to two failure modes. It is assumed that time to failure data are available, but it is unknown for each failure which of the two main failure modes caused the event. A loglikelihood method was developed and tested on generated mixed failure mode data. Shortcomings of this method triggered the development of two additional methods, MINESS and MINESS+, which minimize the error sum of squares of the reliability function, after separating the failure data into two sets.

The brake reaction test performed on a rear axle assembly revealed that the brake flange weld could not sustain the load needed to pass the minimum requirement of the test. Evaluation of the failure mode indicated that the fracture of the weld originated at the root of the weld and cracked through the fusion zone of the weld instead of cracking through base material (toe failure). The paper aims to discuss these issues.

A computational methodology is presented to quantify the critical parameters to prevent throat failure. The torsion dominated loading created high in-plane shear stress on the weld which can contribute significantly to the premature failure.

The failure through the fusion zone, often termed as weld throat/root failure, was not accounted for during the design phase by numerical simulation which led to the wrong conclusion that the design will pass the test requirement. Although weld sizing and weld penetration depth can explain such unexpected failure modes, fatigue life of this particular failure was still over-predicted using the Master SN curve formulation of structural stress approach which is well established for Mode I type of failure. Accounting for the shear component in the structural stress approach led to good correlation with the test specimen. Weld throat depth is a significant parameter contributing to throat failure.

The failure of the weld joining the brake flange and the tube of an axle is a high severity failure mode which can result in loss of vehicle control and injury or death and hence the failure should be prevented at any cost.

Most of the previous work of welded components relates to Mode I loading. There is very few research performed to discuss the Mode III loading and failure. This research illustrates the importance of considering the throat failure mode and quantifies the weld parameters to prevent such failures in design applications.

Studies a non‐homogeneous Poisson process software reliability model with failure rate based on Zipf’s law. Discusses the rate function, mean value function and the estimation of parameters. The proposed model can be used to analyse the reliability growth. The results of applying the proposed model and Duane model to several actual failure data sets show that the model with failure rate observed from Zipf’s law can fit not only in operating software but also in testing software. The result also indicates that the proposed model has better long‐term predictive capability than the Duane model for failure data sets with power law’s failure rates

This paper is to present a new failure model that can be applied to single‐sample failure data of a single system under testing.

The Bayesian method is used for the reliability evaluation. The weighted least squares method is used for determining the parameters of the reliability function.

The authors have observed the operation of a special computer numerical control (CNC) system for a period of over two years, and maintained a reliability database will all the collected failure data, from which the main source of failures can be identified.

Preliminary research results are very encouraging. However, more work will be necessary to validate the new failure model.

The determination of the parameters of the reliability function of a system under testing helps to identify its failure characteristics and potential quality problems.

It is hoped that the paper can help understand some of the challenges in modeling the failure behavior of special CNC systems.

Demonstrates a method for examining service failures and recovery strategies in service industries and provides a typology of service failures and recoveries in the restaurant industry. Based on 373 critical incidents collected from restaurant customers, uses the critical incident technique (CIT) to identify 11 unique failure types and eight different recovery strategies. Additional data regarding the magnitude of the service failure, the service recovery rating, the lapsed time since the failure/recovery incident, and customer retention rates were also collected. Presents this information along with managerial and research implications.

This paper aims to provide a cost effective failure mode and effects analysis tool to overcome the disadvantages of the traditional FMEA that the cost due to failure is not defined.

The method presented in this paper is based on the fuzzy utility theory. It uses utility theory and fuzzy membership functions for the assessment of severity, occurrence, and detection. The utility theory accounts for the nonlinear relationship between the cost due to failure and the ordinal ranking. The application of fuzzy membership functions better represents the team opinions. The Risk Priority Index (RPI) is developed for the prioritization of failure modes.

The advantages of the FUT‐based FMEA are demonstrated through cases studies. It shows that it can take the cost due to failure into account when prioritizing failure modes.

The FUT‐based FMEA presented in this paper provides a convenient cost‐effective tool for failure analysis. It improves the performance FMEA in the risk and failure analysis for product design and manufacturing/assembly process.

The purpose of this paper is to model the occurrences of successive failure types and times to failure of the two repairable machine systems.

Historical data on failure types and time to failures of the given machine systems (4 nos) were gainfully used. Second order time homogeneous Markov Chain models were used to characterize the occurrences of the two broad failure type, namely, mechanical and electrical, after having found that the occurrences of failure types were dependent. Second order time homogeneous Markov Chain with Bivariate Distribution function (M2BVD) was used to model the times between successive failures {T_n, n≥1} for each machine system.

It is possible to apply the theoretical framework of Markov chain models to the accumulated data on failure types and failure times of any repairable system, which provide a wealth of information on the systems and are often left unused.

The framework used in the study can be improved to accommodate multiple failure types and failure times of any repairable system to the extent that a more accurate prediction of these two variables and a better estimate of the system reliability are available.

The models for failure types and failure times of the given machine systems would be of immense use to the maintenance crew for predicting the future failure types and failure times of any given system and subsequently organizing and fine‐tuning their state of preparedness.

Pareto histograms are commonly used to determine maintenance priorities by ranking equipment failure codes according to their relative cost or downtime contribution. However, such histograms do not readily enable identification of the dominant variables influencing downtime and repair costs, namely the failure frequency, mean downtime and mean repair cost associated with each failure code. Advances an alternative method for analysing equipment downtime and repair costs using logarithmic (log) scatterplots. By applying limit values, log scatterplots can be divided into four quadrants enabling failures to be classified according to acute or chronic characteristics and facilitating root cause failure analysis. Log scatterplots permit the identification of frequently occurring failures that consume relatively little repair cost or downtime yet cause frequent operational disturbances leading to production losses. In addition, by graphing the trend of failure data over successive time periods, log scatterplots provide a useful visual means of evaluating the performance of maintenance improvement initiatives. Provides examples of the practical application of log scatterplots by a number of mining companies and mining equipment suppliers in Chile.