A New Model for Repairable Systems with Nonmonotone Intensity Function

2014 ◽  
Vol 31 (8) ◽  
pp. 1553-1563 ◽  
Author(s):  
Fu-Kwun Wang ◽  
Yi-Chen Lu
Keyword(s):  
Intensity Function ◽  
Repairable Systems ◽  
New Model

scholarly journals Reliability Analysis of Repairable Systems Based on a Two-Segment Bathtub-Shaped Failure Intensity Function

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 52374-52384 ◽  
Author(s):  
Xuejiao Du ◽  
Zhaojun Yang ◽  
Chuanhai Chen ◽  
Xiaoxu Li ◽  
Michael G. Pecht
Keyword(s):  
Reliability Analysis ◽  
Intensity Function ◽  
Repairable Systems ◽  
Failure Intensity

On the Estimation of Failure Rates of Multiple Pipeline Systems

2006 ◽  
Author(s):  
F. Caleyo ◽  
L. Alfonso ◽  
J. A. Alca´ntara ◽  
J. M. Hallen ◽  
F. Ferna´ndez Lagos ◽  
...  
Keyword(s):  
Stochastic Model ◽  
Oil And Gas ◽  
Statistical Tests ◽  
Real Life ◽  
Intensity Function ◽  
Model Parameters ◽  
Repairable Systems ◽  
Failure Data ◽  
Pipeline Systems ◽  
Failure Intensity

In this work, the statistical methods for the reliability of repairable systems have been used to produce a methodology capable to estimate the annualized failure rate of a pipeline population from the historical failure data of multiple pipelines systems. The proposed methodology provides point and interval estimators of the parameters of the failure intensity function for two of the most commonly applied stochastic models; the homogeneous Poisson process and the power law process. It also provides statistical tests to assess the adequacy of the stochastic model assumed for each system and to test whether all systems have the same model parameters. In this way, the failure data of multiple pipeline systems are only pooled to produce a generic failure intensity function when all systems follow the same stochastic model. This allows addressing both statistical and tolerance uncertainty adequately. The proposed methodology is outlined and illustrated using real life failure data of multiple oil and gas pipeline systems.

On the Estimation of Failure Rates of Multiple Pipeline Systems

2008 ◽  
Vol 130 (2) ◽  
Author(s):  
F. Caleyo ◽  
L. Alfonso ◽  
J. Alcántara ◽  
J. M. Hallen
Keyword(s):  
Stochastic Model ◽  
Oil And Gas ◽  
Statistical Tests ◽  
Real Life ◽  
Intensity Function ◽  
Model Parameters ◽  
Repairable Systems ◽  
Failure Data ◽  
Pipeline Systems ◽  
Failure Intensity

In this work, the statistical methods for the reliability of repairable systems have been used to produce a methodology capable to estimate the annualized failure rate of a pipeline population from the historical failure data of multiple pipeline systems. The proposed methodology provides point and interval estimators of the parameters of the failure intensity function for two of the most commonly applied stochastic models: the homogeneous Poisson process and the power law process. It also provides statistical tests for assessing the adequacy of the stochastic model assumed for each system and testing whether all systems have the same model parameters. In this way, the failure data of multiple pipeline systems are only merged in order to produce a generic failure intensity function when all systems follow the same stochastic model. This allows statistical and tolerance uncertainties to be addressed adequately. The proposed methodology is outlined and illustrated using real-life failure data of oil and gas pipeline systems.

Risk-based threshold on intensity function of repairable systems: A case study on aero engines

2020 ◽  
pp. 2150020
Author(s):  
Garima Sharma ◽  
Rajiv Nandan Rai
Keyword(s):  
Intensity Function ◽  
Repairable Systems ◽  
Financial Loss ◽  
Failure Data ◽  
Combat Aircraft ◽  
Military Aviation ◽  
Periodic Maintenance ◽  
Aero Engines ◽  
Field Failure

Maintenance, repair and overhaul (MRO) facilities deal with situations where repairable systems and its components are required to be designated as high failure rate components (HFRCs). The shortlisted HFRCs are then selected for reliability improvement. The procedure of short listing components as HFRCs is commonly based on experts’ field experience or number of failures. In case of organizations dealing with complex and critical repairable systems like military aviation (MA) and nuclear industries, the subjectivity in the short listing of HFRCs can lead to prolonged unavailability of equipment and may incur financial loss. Thus, a scientific methodology is required to be developed for HFRC designation. The paper develops a methodology for HFRC designation through risk-based threshold on intensity function by considering combat aircraft engines as a case. To develop the threshold methodology, the paper uses generalized renewal process (GRP) for multiple repairable systems (MRS) considering both corrective and preventive maintenance as imperfect. The proposed methodology is duly validated with the help of field failure data of two variants of the same aero engine of a particular combat aircraft. The developed methodology in this paper is highly inspired by the problems faced by the various industries while operating the repairable systems and can be extended for systems which undergo periodic maintenance, repair and overhaul.

Availability Research on K-out-of-N: G Systems with Repair Time Omission

2010 ◽  
Vol 118-120 ◽  
pp. 342-347
Author(s):  
Zhi Yu Jia ◽  
Rui Kang ◽  
Li Chao Wang ◽  
Nai Chao Wang
Keyword(s):  
Steady State ◽  
Random Variable ◽  
Threshold Value ◽  
Repair Time ◽  
Repairable Systems ◽  
System Operation ◽  
Numerical Examples ◽  
New Model ◽  
Reliability Indices ◽  
New Models

Based on some practical problems in maintenance, a new model for K-out-of-N Markov repairable systems is introduced in this paper. The model focuses on that repair times that are sufficiently short (less than some threshold value) do not affect the system operation. We can say that such a repair time is omitted from the downtime record, and the system can be considered as being operating during this repair time. A model is built in which the threshold value is regarded as a constant at first. And then the model is generalized to allow the threshold value to be a non-negative random variable. Both instantaneous availability and steady-state availability are calculated for these new models as reliability indices. Some numerical examples are presented to verify the validity of these models.

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