International Journal of Reliability, Risk and Safety: Theory and Application
Volume:3 Issue: 2, Jun 2020

In this paper, we consider the estimation problem in the presence of masked data for series systems. A missing indicator is proposed to describe masked set of each failure time.Moreover, a Generalized Linear model (GLM) with appropriate link function is used to model masked indicator in order to involve masked information into likelihood function. Both maximum likelihood and Bayesian methods were considered.The likelihood function with both missing at random (MAR) and missing not at random (MNAR) mechanismsare derived.Using an auxiliary variable, a Bayesian approach is expanded to obtain posterior estimations of the model parameters.The proposed methods have been illustrated through a real example.

Deformations occur gradually in the gas turbine components since they are working under high temperature and stress. In the turbine blade alloys, creep is the most significant failure mechanism. In this research, creep life has been estimated for the blade alloys by considering humidity. A method is proposed to estimate the creep life by direct consideration of humidity on the creep life of the gas turbine blade. In the proposed model, the humidity factor is added to the classic Larson Miller creep life estimation method. This model is capable of predicting creep life with known dry temperature (Water Air Ratio=0), mechanical stress, and humidity. In this approach, there is no need to measure blade temperature variation during operation. As a case study, the creep life of first-stage turbine blade alloy is predicted using the proposed method and benchmarked with published (Finite element analysis) FEA results. The reliability of the blades was estimated by considering different success criteria using Monte Carlo simulation. The reliability of the creep rupture life of Nimonic-90 steel was carried out using SCRI mode based on the Z-parameter. The scattered data has been considered for creep rupture of materials in this part. The results show that creep life increases with humidity increase. It is also shown that with an increase in mechanical stress and temperature fluctuations, the reliability of the turbine blade creep life decreases sharply.

The model-based fault detection approach is one of the software-based supervision systems monitoring. This method has a marked effect to detect components fault without demanding extra sensors to measure or add redundancy. The extended multiple model’s adaptive estimation method is an online strategy to detect and isolation failure of components. Simple implementation, fast and accurate response, compatibility with nonlinear systems, and the ability to detect different types of faults are the most important features of this method.  This method is applied to the faulty spacecraft in terms of actuators and its capability is evaluated. The most probable actuator fault implemented using MATLAB/SIMULINK software. The presented approach successfully detects faulty actuators.

Queueing theory is a way for real-world problems modeling and analyzing. In many processes, the input is converted to the desired output after several successive steps. But usually limitations and conditions such as Lack of space, feedback, vacation, failure, repair, etc. have a great impact on process efficiency. This article deals with the modeling the steady-state behavior of an M^X/G/ 1 retrial queueing system with k phases of service. The arriving batches join the system with dependent admission due to the server state. If the customers find the server busy, they join the orbit to repeat their request. Although, the first phase of service is essential for all customers, any customer has three options after the completion of the i-th phase (i=1,2,…,k). They may take the (i+1)-th phase of service with probability θ_i, otherwise return the orbit with probability p_i or leave the system with probability (〖1-p〗_i-θ_i). Also, after each phase, the probabilistic failure, delay, repair and vacation is considered. In this article, after finding the steady-state distributions, the probability generating functions of the system and orbit size have been found. Then, some important performance measures of the system have been derived. Also, the system reliability has been defined. Eventually, to demonstrate the capability of the proposed model, the sensitivity analysis of performance measures via some model parameters (arrival/retrial/vacation rate) in different reliability levels have been investigated in a specific case of this model. Additionally, for optimizing the performance of system, some technical suggestions are presented.

Torsional vibration (TV) is one of the major issues and very important calculation for the safe running of internal combustion engines, specifically crankshaft. The properties of parts connected to the crankshaft have significant effect on vibration of the system as well as the crankshaft life. Initial selection of this part is usually specified based on engine designer experience and also the torsional vibration calculation of the crank train. In this paper, the focus is to find optimum tuned mass to connect to the crankshaft from the damper side using CAE tools. It is a mounting disk at the free end of the crankshaft named tuned mass. Therefore, the effect of tuned mass inertia on design criteria, especially crankshaft life, was investigated. The results show high sensitivity of high cycle fatigue safety factor of crankshaft to tuned mass. Therefore, adding a suitable tuned mass to the system can increase the crankshaft life, when needed. The results were presented in the paper in detail

We consider life extension for a class of coherent system consisting of independent components with an increasing failure rate functions. The maintenance action is applied in a fixed component called the target component. To this end, the minimal repair and cold standby actions are provided. We also consider two alternative policies for the target component. A component following a new random variable, and another following the same distributions of the target component. These policies obviously increase the reliability and life of the target component and consequently, the life and reliability of a coherent system are also increased. In this regard, the life of the system is also extended. Some numerical results considering these life extensions are presented.

This paper describes the use of the Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method for predicting the risk of leakage in a storage tank. This is the first time AHP and PROMETHEE have been used in this way. Important decisions about day to day operations are continually made in a petroleum environment. Storage tanks in refineries contain large volumes of flammable and hazardous liquids. Decision processes need to evaluate and select alternatives with a higher probability of resulting in a hazard, among many different alternatives. The new model described in this paper will aid decision-makers to predict which tank is likely to develop a leak and determine what criteria (source of risk) could result in a leak. Although the case study deals with a specific risk prediction problem, the combination of AHP and PROMETHEE methods can be applied to other decision problems.

This paper proposes a risk assessment method for estimating theCOVID-19 Infection risk index in flight destinations based on the pair wise comparison method to solve the problem of health monitoring devices shortage in airlines. In this research, Kish Airlines flight destinations are considered as a case study. By considering the importance of continuing air travel during COVID-19 pandemic, one of the most effective ways for decreasing the risk of infection to COVID-19 in air travel is establishing health monitoring stations at the airport gates. Because of the enormous number of airports and airline routes, nationwide coverage of them by the establishment of the health monitoring stations is unimaginable. Therefore, in this paper, the pair wise comparison method used for evaluating COVID-19 infection risk index in selected flight destinations and to evaluate the optimal policy for allocation of health monitoring equipment in Kish Airline destinations a geostatistical map is designed based on the calculated infection risk score.

Reliability growth is the positive improvement in a product’s criteria (or parameter) over a period of time due to changes in the design or product process. By analyzing the growth of reliability in a system, it can be seen that at a certain stage of the epidemic, the growth of the transmission and the rate of infection change over time. During the spread of disease, problem areas are identified and knowledge of the disease increased and then initial treatment and tools may be redesigned or reprocessed to take appropriate corrective action. In other words, each stage of the spread of the disease has a different level of growth transmission depending on appropriate corrective action. Therefore, according to this case, there are conditions under which phenomena can be described by Non-Homogeneous Poisson Process (NHPP). However, traditional epidemiological models based on exponential distribution are not able to predict disease growth during different stages of the outbreak. Therefore, in this paper, the Piecewise Crow-AMSAA (NHPP) model, which is based on the Non-Homogeneous Poisson process, is used to predict the growth of infected cases and deaths of Coronavirus outbreak. Initially, the Iran cumulative confirmed case and death data are divided into several sections based on the manual separation to find out each different infection phase at each different time period. Then Crow-AMSAA (NHPP) model is applied to the segmented data. At each stage of the outbreak, the model parameters are estimated independently using the maximum likelihood estimation (MLE) technique. Finally, the growth parameters in each stage are compared with each other and external and environmental factors are identified and examined.

This paper investigates the design of a classifier that effectively identifies undesired events by detecting patterns in the pressure signal of a compressed air system using a continuous wavelet transform. The pressure signal of a compressed air system carries useful information about operational events. These events form patterns that can be used as ‘signatures’ for event detection. Such patterns are not always apparent in the time domain and hence the signal was transformed to the time-frequency domain. Data was collected using an industrial compressed air system with load/unload control.  Three different operating modes were considered: idle, tool activation , and faulty. The wavelet transforms of the pressure signal revealed unique features to identify events within each mode. A neural network classifier was created to detect faulty compressed air system behaviourbehaviour. Future work will investigate the detection of more faults and using other classification algorithms.

This paper presents the graphical evaluation and review technique (GERT) exploration of performance measures for lot acceptance sampling procedures having attribute characteristics following life tests based on percentiles of Rayleigh Distribution and henceforth determining optimum sampling size.  The advantageous implications of GERT analysis in this framework is primarily to visualize the dynamics of the sampling inspection system and secondly, critical analysis of sampling procedure characteristics. The formula of operating characteristics (OC) function and average sample number (ASN) function is derived and illustrated numerically. Lastly, tables have been provided to determine the optimum sample size assuring certain mean life or quality of the product.

Modern engineering systems have proven to be quite complex due to the involvement of uncertainties and a number of dependencies among the system components. Shortcoming in the inclusion of such complex features results in the wrong assessment of reliability and safety of the system, ultimately to the incorrect engineering decisions. In this paper, the usefulness of Bayesian Networks (BNs) for achieving improved modeling and reliability and  risk analysis is investigated. The calculation of a number of Importance Measures with use of Fault Tree Analysis as well as BNs is provided for a complicated railway operation problem. The BNs based safety risk model is investigated in terms of quantitative reliability and safety analysis as well as for multi dependencies and uncertainty modeling.