Reliability models of repairable systems considering the effect of operating conditions

2003 ◽  
Author(s):  
P.V.N. Prasad ◽  
K.R.M. Rao
Keyword(s):  
Operating Conditions ◽  
Repairable Systems ◽  
Reliability Models

Reliability Analysis of Repairable Systems Subject to System Modifications

1998 ◽  
Author(s):  
Z. H. Jiang ◽  
L. H. Shu ◽  
B. Benhabib
Keyword(s):  
Life Cycle ◽  
Steady State ◽  
Cost Estimation ◽  
Life Cycle Cost ◽  
Repairable Systems ◽  
Environmentally Conscious ◽  
Reliability Indices ◽  
Reliability Models ◽  
System Reconfiguration ◽  
Environmentally Conscious Design

Abstract This paper approaches environmentally conscious design by further developing a reliability model that facilitates design for reuse. Many reliability models are not suitable for describing systems that undergo repairs performed during remanufacture and maintenance because the models do not allow the possibility of system reconfiguration. In this paper, expressions of reliability indices of a model that allows system reconfiguration are developed to enable life-cycle cost estimation for repairable systems. These reliability indices of a population of repairable systems are proven theoretically to reach steady state. The expressions of these indices at steady state are obtained to gain insight into the model behavior, and to facilitate life-cycle cost estimation.

Optimal Reliability and Cost of Non-Repairable Systems Subject to Two Failure Modes Considering Correlated Failures

2018 ◽  
Vol 25 (05) ◽  
pp. 1850024
Author(s):  
Anusha Krishna Murthy ◽  
Saikath Bhattacharya ◽  
Lance Fiondella
Keyword(s):  
Failure Mode ◽  
System Reliability ◽  
Failure Modes ◽  
Optimal Number ◽  
Optimal Designs ◽  
Repairable Systems ◽  
Independent Manner ◽  
Reliability Models ◽  
Correlated Failures ◽  
The Impact

Most reliability models assume that components and systems experience one failure mode. Several systems such as hardware, however, are prone to more than one mode of failure. Past two-failure mode research derives equations to maximize reliability or minimize cost by identifying the optimal number of components. However, many if not all of these equations are derived from models that make the simplifying assumption that components fail in a statistically independent manner. In this paper, models to assess the impact of correlation on two-failure mode system reliability and cost are developed and corresponding expressions for reliability and cost optimal designs derived. Our illustrations demonstrate that, despite correlation, the approach identifies reliability and cost optimal designs.

A Simulation-Based Random Process Method for Time-Dependent Reliability of Dynamic Systems

2010 ◽  
Author(s):  
Amandeep Singh ◽  
Zissimos P. Mourelatos ◽  
Efstratios Nikolaidis
Keyword(s):  
Random Process ◽  
Operating Conditions ◽  
Time Dependent ◽  
Process Time ◽  
Cumulative Probability ◽  
Repairable Systems ◽  
First Passage ◽  
True Value ◽  
Warranty Costs ◽  
Short Time

Reliability is an important engineering requirement for consistently delivering acceptable product performance through time. As time progresses, a product may fail due to time-dependent operating conditions and material properties, and component degradation. The reliability degradation with time may significantly increase the lifecycle cost due to potential warranty costs, repairs and loss of market share. In this work, we consider the first-passage reliability, which accounts for the first time failure of non-repairable systems. Methods are available that provide an upper bound to the true reliability, but they may overestimate the true value considerably. This paper proposes a methodology to calculate the cumulative probability of failure (probability of first passage or upcrossing) of a dynamic system with random properties, driven by an ergodic input random process. Time series modeling is used to characterize the input random process based on data from a “short” time period (e.g. seconds) from only one sample function of the random process. Sample functions of the output random process are calculated for the same “short” time because it is usually impractical to perform the calculation for a “long” duration (e.g. hours). The proposed methodology calculates the time-dependent reliability, at a “long” time using an accurate “extrapolation” procedure of the failure rate. A representative example of a quarter car model subjected to a stochastic road excitation demonstrates the improved accuracy of the proposed method compared with available methods.

scholarly journals Impact of Early Life Failures in Services of Engineering Asset Fleets

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Arinan Dourado ◽  
Felipe A. C. Viana
Keyword(s):  
Infant Mortality ◽  
Service Providers ◽  
Financial Burden ◽  
Operating Conditions ◽  
Reliability Modeling ◽  
Fleet Size ◽  
Reliability Models ◽  
The Impact ◽  
Original Equipment Manufacturers ◽  
Manufacturing Problems

Services and warranties of large fleets of engineering assets is a very profitable business where original equipment manufacturers and independent service providers offer contracts designed to cover events in day-to-day service as well as major maintenance and repairs over the life of the asset. Accurate reliability modeling, as a way to understand how the complex stochastic interactions between operating conditions and component capability define useful life, is key for services profitability. The modeling task is daunting as factors such as aggressive mission mixes introduced by operators, exposure to harsh environment, inadequate maintenance, and problems with mass production (bad batch of materials) can lead to large discrepancies between designed and observed useful lives. This paper is focused on how to quantify the impact of infant mortality in fleets of industrial assets. A simple numerical experiment is used to address the fundamental question: how does number of observations and fleet size interact with each other in fleet management? The results demonstrate that material capability, penetration of bad batch of material in the fleet, and commissioning time can drastically influence fleet unreliability. Moreover, infant mortality due to manufacturing problems/material capability is a manifestation of an outlier problem. As a consequence, the propensity to observe first failures depend on the actual fleet size. Since failure observations are used to build/update the reliability models, small fleet operators have to deal with large uncertainties when quantifying infant mortality. This impacts their ability to make provisions for service and maintenance (inventory, labor, loss of productivity, etc.). Although the large number of failure observations causes a financial burden in large fleet operators, it also allows for reduced uncertainty in building/updating the reliability models. In turn, this improves their ability to forecast future failures and make provisions for service and maintenance.

scholarly journals Impact of Bias Temperature Instabilities on the Performance of Logic Inverter Circuits Using Different SiC Transistor Technologies

Crystals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1150
Author(s):  
Yoanlys Hernandez ◽  
Bernhard Stampfer ◽  
Tibor Grasser ◽  
Michael Waltl
Keyword(s):  
Delay Time ◽  
Propagation Delay ◽  
Operating Conditions ◽  
Physical Models ◽  
Stable Operation ◽  
Reliability Models ◽  
Bias Temperature Instability ◽  
Propagation Delay Time ◽  
Aging Mechanisms ◽  
The Impact

All electronic devices, in this case, SiC MOS transistors, are exposed to aging mechanisms and variability issues, that can affect the performance and stable operation of circuits. To describe the behavior of the devices for circuit simulations, physical models which capture the degradation of the devices are required. Typically compact models based on closed-form mathematical expressions are often used for circuit analysis, however, such models are typically not very accurate. In this work, we make use of physical reliability models and apply them for aging simulations of pseudo-CMOS logic inverter circuits. The model employed is available via our reliability simulator Comphy and is calibrated to evaluate the impact of bias temperature instability (BTI) degradation phenomena on the inverter circuit’s performance made from commercial SiC power MOSFETs. Using Spice simulations, we extract the propagation delay time of inverter circuits, taking into account the threshold voltage drift of the transistors with stress time under DC and AC operating conditions. To achieve the highest level of accuracy for our evaluation we also consider the recovery of the devices during low bias phases of AC signals, which is often neglected in existing approaches. Based on the propagation delay time distribution, the importance of a suitable physical defect model to precisely analyze the circuit operation is discussed in this work too.

scholarly journals Simple formulae for counting processes in reliability models

1997 ◽  
Vol 29 (04) ◽  
pp. 1018-1038 ◽  
Author(s):  
James Ledoux ◽  
Gerardo Rubino
Keyword(s):  
Queueing Theory ◽  
Marginal Distribution ◽  
Arrival Process ◽  
Counting Processes ◽  
Repairable Systems ◽  
Reliability Models ◽  
Dependability Evaluation ◽  
Mission Period ◽  
Analytical Expressions

Dependability evaluation is a basic component in the assessment of the quality of repairable systems. We develop a model taking simultaneously into account the occurrence of failures and repairs, together with the observation of user-defined success events. The model is built from a Markovian description of the behavior of the system. We obtain the distribution function of the joint number of observed failures and of delivered services on a fixed mission period of the system. In particular, the marginal distribution of the number of failures can be directly related to the distribution of the Markovian arrival process extensively used in queueing theory. We give both the analytical expressions of the considered distributions and the algorithmic solutions for their evaluation. An asymptotic analysis is also provided.

Some Reliability Models for Analyzing the Effect of Operating Conditions

1997 ◽  
Vol 04 (02) ◽  
pp. 133-148 ◽  
Author(s):  
Dhananjay Kumar ◽  
Ulf Westberg
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Operating Conditions ◽  
Accelerated Failure Time ◽  
Data Set ◽  
Proportional Hazards Models ◽  
Reliability Models ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

Basic approaches of some of the reliability models available for analyzing the effect of operating conditions (or covariates) on the lifetime of a system are shortly discussed, and a general guideline for how to select an appropriate model for a given data set is provided. Some of the models have theoretical and computational difficulties which make them difficult to apply. Models that appear to be suitable for practical applications can broadly be classified as the class of proportional hazards models and the class of accelerated failure time models. In the class of proportional hazards models, e.g. the proportional hazards model and the proportional odds model, the effect of the covariates is assumed to act multiplicatively on the hazard rate or its transformations. In the class of accelerated failure time models, e.g. the parametric regression models, the effect of the covariates is assumed to act multiplicatively on the failure time or its transformations. Models from the proportional hazards family appear to be the better ones for analyzing the effect of the covariates due to the method used for estimating the parameters of these models.

Performance of Reliability Models for Repairable Systems: Dealing with Information Uncertainty

2020 ◽  
Author(s):  
Olivier Blancke ◽  
Gabriel McCarthy ◽  
Mathieu Payette ◽  
Lucie Bibeau ◽  
Jean-Francois Boudreau ◽  
...  
Keyword(s):  
Information Uncertainty ◽  
Repairable Systems ◽  
Reliability Models

Steady-State Reliability Analysis of Repairable Systems Subject to System Modifications

1999 ◽  
Vol 121 (4) ◽  
pp. 614-621 ◽  
Author(s):  
Z. H. Jiang ◽  
L. H. Shu ◽  
B. Benhabib
Keyword(s):  
Steady State ◽  
Repairable Systems ◽  
Environmentally Conscious ◽  
Reliability Indices ◽  
Reliability Models ◽  
System Reconfiguration ◽  
Simulation Results ◽  
Environmentally Conscious Design ◽  
Design For Reuse ◽  
Further Development

Environmentally conscious design is approached through analysis and further development of a reliability model that facilitates design for reuse of products. Many reliability models may not he suitable for describing systems that undergo repairs performed during remanufacture and maintenance since they do not allow for the possibility of system reconfiguration. In this paper, expressions of reliability indices of a model that allows system modifications during repair are derived. These reliability indices that describe a population of repairable systems are theoretically proven to reach steady state, supporting the simulation results of the model. This model can be used to estimate life-cycle replacement requirements for systems that are remanufactured, thereby facilitating decisions during system design and use. An example illustrates the application of the model to a relevant industry.

Counting Failure Events for a Support System

2012 ◽  
Author(s):  
Curtis Smith
Keyword(s):  
Support System ◽  
Distribution System ◽  
Support Systems ◽  
Cooling Water ◽  
Operating Conditions ◽  
System Failure ◽  
Repairable Systems ◽  
Component Level ◽  
System Failures ◽  
Operational Envelope

An initiating event is a departure from a desired operational envelope to a system state where a control response is required either by human or machine intervention. In the case of a support system such as a cooling water or electrical distribution system, failure of this system represents the departure from normal operating conditions. Initiating event frequencies for probabilistic risk assessments are generally based on data collection. For rare, but potentially high consequence initiators representing the failure of support systems, this approach has a number of shortcomings. For example, since events are rare, there may not be any complete system failure events in the available data sets. Consequently, there is a desire to model system failures of initiating events since component-level failure events in the support systems are more frequent. Therefore, what is needed is a suitable method for calculating the initiating event frequency (expected number of system failures over some operating mission) from the relatively well known component failure rates. In this paper, we explore ways to develop and quantify models that represent the rates of failures for support systems. These failures of repairable systems can be represented by focusing on observables. Specifically, we can either count the number of failures in time t or count the times of failure. As part of the analysis, we will describe and evaluate a couple of typical redundant support systems. Included in the analysis will be considerations of dependent failure mechanisms.

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