Assessment of extreme climatic event model parameters estimation techniques: a case study using Tasmanian extreme rainfall

One can find many reliability, availability, and maintainability (RAM) models proposed in the literature. However, such models become more complex day after day, as there is an attempt to capture equipment performance in a more realistic way, such as, explicitly addressing the effect of component ageing and degradation, surveillance activities, and corrective and preventive maintenance policies. Then, there is a need to fit the best model to real data by estimating the model parameters using an appropriate tool. This problem is not easy to solve in some cases since the number of parameters is large and the available data is scarce. This paper considers two main failure models commonly adopted to represent the probability of failure on demand (PFD) of safety equipment: (1) by demand-caused and (2) standby-related failures. It proposes a maximum likelihood estimation (MLE) approach for parameter estimation of a reliability model of demand-caused and standby-related failures of safety components exposed to degradation by demand stress and ageing that undergo imperfect maintenance. The case study considers real failure, test, and maintenance data for a typical motor-operated valve in a nuclear power plant. The results of the parameters estimation and the adoption of the best model are discussed.

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Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review

Applied Energy ◽

10.1016/j.apenergy.2015.05.035 ◽

2015 ◽

Vol 154 ◽

pp. 500-519 ◽

Cited By ~ 245

Author(s):

Vun Jack Chin ◽

Zainal Salam ◽

Kashif Ishaque

Keyword(s):

Parameters Estimation ◽

Model Parameters ◽

Estimation Techniques

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Vulnerability and impact assessment of extreme climatic event: A case study of southern Punjab, Pakistan

The Science of The Total Environment ◽

10.1016/j.scitotenv.2016.11.155 ◽

2017 ◽

Vol 580 ◽

pp. 468-481 ◽

Cited By ~ 19

Author(s):

Abdul Qayyum Aslam ◽

Sajid R Ahmad ◽

Iftikhar Ahmad ◽

Yawar Hussain ◽

Muhammad Sameem Hussain

Keyword(s):

Impact Assessment ◽

Extreme Climatic Event ◽

Climatic Event

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Reliability Assessment of Degradable Systems under Imperfect Maintenance and Utilisation Rate: A Case Study

International Journal of Engineering Research in Africa ◽

10.4028/www.scientific.net/jera.26.184 ◽

2016 ◽

Vol 26 ◽

pp. 184-194 ◽

Cited By ~ 3

Author(s):

Hassana Mahfoud ◽

Abdellah El Barkany ◽

Ahmed El Biyaali

Keyword(s):

Delay Time ◽

Goodness Of Fit ◽

Failure Process ◽

Estimation Method ◽

Parameters Estimation ◽

Utilization Rate ◽

Model Parameters ◽

Proportional Delay ◽

Imperfect Maintenance

Medical equipment is the biggest capital investment of every healthcare industry and ensuring the reliability and maintenance for critical devices is vital for Patient/user safety and better availability of services. To keep the medical device in a good operational condition, Inspection based maintenance activities have been the most usual means. The purpose of this study is to establish an improved delay time framework to model the two-stage failure process taking into account the influence of the utilization rate on the system’s degradation on the assumption of imperfect maintenance at inspection. This proportional delay time model is seldom considered in DTMs widely used in literature. A complete framework, for Parameters estimation method and the test for goodness of fit is given. To illustrate the model capabilities, a reel case study from the healthcare domain is presented, the model parameters are estimated entirely using the collected maintenance data. Then, the maximum likelihood estimation of the reliability parameters is achieved by Genetic Algorithms.

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UAV dynamics model parameters estimation techniques: A comparison study

2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) ◽

10.1109/aeect.2013.6716436 ◽

2013 ◽

Cited By ~ 3

Author(s):

Mohammad A. Al-Shabi ◽

Khaled S. Hatamleh ◽

Asad A. Asad

Keyword(s):

Parameters Estimation ◽

Model Parameters ◽

Comparison Study ◽

Dynamics Model ◽

Estimation Techniques

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Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

Journal of Hydroinformatics ◽

10.2166/hydro.2008.016 ◽

2008 ◽

Vol 10 (2) ◽

pp. 153-162 ◽

Cited By ~ 2

Author(s):

B. G. Ruessink

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Parameter Estimates ◽

Model Parameters ◽

Parameter Stability ◽

Small Uncertainty ◽

Best Fit ◽

Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

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Impacts of Polarimetric Radar Observations on Hydrologic Simulation

Journal of Hydrometeorology ◽

10.1175/2010jhm1218.1 ◽

2010 ◽

Vol 11 (3) ◽

pp. 781-796 ◽

Cited By ~ 22

Author(s):

Jonathan J. Gourley ◽

Scott E. Giangrande ◽

Yang Hong ◽

Zachary L. Flamig ◽

Terry Schuur ◽

...

Keyword(s):

Pearson Correlation ◽

Extreme Rainfall ◽

Rain Gauge ◽

Hydrologic Model ◽

Laboratory Research ◽

Model Parameters ◽

Polarimetric Radar ◽

Storm Events ◽

Hydrologic Simulation ◽

Radar Observations

Abstract Rainfall estimated from the polarimetric prototype of the Weather Surveillance Radar-1988 Doppler [WSR-88D (KOUN)] was evaluated using a dense Micronet rain gauge network for nine events on the Ft. Cobb research watershed in Oklahoma. The operation of KOUN and its upgrade to dual polarization was completed by the National Severe Storms Laboratory. Storm events included an extreme rainfall case from Tropical Storm Erin that had a 100-yr return interval. Comparisons with collocated Micronet rain gauge measurements indicated all six rainfall algorithms that used polarimetric observations had lower root-mean-squared errors and higher Pearson correlation coefficients than the conventional algorithm that used reflectivity factor alone when considering all events combined. The reflectivity based relation R(Z) was the least biased with an event-combined normalized bias of −9%. The bias for R(Z), however, was found to vary significantly from case to case and as a function of rainfall intensity. This variability was attributed to different drop size distributions (DSDs) and the presence of hail. The synthetic polarimetric algorithm R(syn) had a large normalized bias of −31%, but this bias was found to be stationary. To evaluate whether polarimetric radar observations improve discharge simulation, recent advances in Markov Chain Monte Carlo simulation using the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) were used. This Bayesian approach infers the posterior probability density function of model parameters and output predictions, which allows us to quantify HL-RDHM uncertainty. Hydrologic simulations were compared to observed streamflow and also to simulations forced by rain gauge inputs. The hydrologic evaluation indicated that all polarimetric rainfall estimators outperformed the conventional R(Z) algorithm, but only after their long-term biases were identified and corrected.

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