An empirical Bayesian based approach to delay time inspection model parameters estimation using both subjective and objective data

2007 ◽  
Vol 23 (1) ◽  
pp. 95-105 ◽  
Author(s):  
W. Wang ◽  
X. Jia
Keyword(s):  
Delay Time ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Objective Data ◽  
Empirical Bayesian

scholarly journals Parameter Estimation of a Delay Time Model of Wearing Parts Based on Objective Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Y. Tang ◽  
J. J. Jing ◽  
Y. Yang ◽  
C. Xie
Keyword(s):  
Parameter Estimation ◽  
Delay Time ◽  
Failure Time ◽  
Likelihood Function ◽  
Distribution Functions ◽  
Model Parameters ◽  
Objective Data ◽  
Time Model ◽  
Gas Drilling ◽  
Renewal Events

The wearing parts of a system have a very high failure frequency, making it necessary to carry out continual functional inspections and maintenance to protect the system from unscheduled downtime. This allows for the collection of a large amount of maintenance data. Taking the unique characteristics of the wearing parts into consideration, we establish their respective delay time models in ideal inspection cases and nonideal inspection cases. The model parameters are estimated entirely using the collected maintenance data. Then, a likelihood function of all renewal events is derived based on their occurring probability functions, and the model parameters are calculated with the maximum likelihood function method, which is solved by the CRM. Finally, using two wearing parts from the oil and gas drilling industry as examples—the filter element and the blowout preventer rubber core—the parameters of the distribution function of the initial failure time and the delay time for each example are estimated, and their distribution functions are obtained. Such parameter estimation based on objective data will contribute to the optimization of the reasonable function inspection interval and will also provide some theoretical models to support the integrity management of equipment or systems.

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

2016 ◽  
Vol 26 ◽  
pp. 184-194 ◽  
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.

A COMPARATIVE STUDY OF PHENOMENOLOGICAL MODELS OF MR BRAKE BASED ON NEURAL NETWORKS APPROACH

2013 ◽  
Vol 11 (01) ◽  
pp. 1350009 ◽  
Author(s):  
WITOLD PAWLUS ◽  
HAMID REZA KARIMI
Keyword(s):  
Neural Networks ◽  
Vibration Suppression ◽  
Feedforward Neural Networks ◽  
Active Suspension ◽  
Full Scale ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Systems Identification ◽  
Active Vibration ◽  
Models Validation

In this paper a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system is modeled and simulated. Two well-known phenomenological hysteresis models are explored: Bouc–Wen and Dahl ones. In particular, influence of their parameters on the response is evaluated and assessed. The next step is to introduce the artificial neural networks and discuss their application in the field of systems identification. Subsequently, two feedforward neural networks are created and trained to estimate parameters characterizing each of the MR damper models described. The semi-active suspension (SAS) system equipped with a MR brake is described and the detailed procedure for acquisition of the reference data used in the models validation stage is elaborated. The models outputs obtained by simulating them with the values of coefficients as identified by the networks are compared to each other as well as to the reference experimental data. Thanks to that, the comparative analysis between the suggested vibration suppression models and the full-scale MR brake is done and it is concluded which of the discussed models has a better performance. The usability of neural networks in the field of parameters estimation of the mathematical models of the real world phenomena is described as well. The novelty of the presented methodology is the application of artificial intelligence methods to estimate model parameters of a MR brake utilized in a SAS system. The results of this approach have a strong potential to be successfully implemented in the area of model-based control of semi-active vibration suppression systems.

scholarly journals Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins

2011 ◽  
Vol 8 (4) ◽  
pp. 7017-7053 ◽  
Author(s):  
Z. Bao ◽  
J. Liu ◽  
J. Zhang ◽  
G. Fu ◽  
G. Wang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Simulation ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Ungauged Catchments ◽  
Vic Model ◽  
Predictions In Ungauged Basins

Abstract. Equifinality is unavoidable when transferring model parameters from gauged catchments to ungauged catchments for predictions in ungauged basins (PUB). A framework for estimating the three baseflow parameters of variable infiltration capacity (VIC) model, directly with soil and topography properties is presented. When the new parameters setting methodology is used, the number of parameters needing to be calibrated is reduced from six to three, that leads to a decrease of equifinality and uncertainty. This is validated by Monte Carlo simulations in 24 hydro-climatic catchments in China. Using the new parameters estimation approach, model parameters become more sensitive and the extent of parameters space will be smaller when a threshold of goodness-of-fit is given. That means the parameters uncertainty is reduced with the new parameters setting methodology. In addition, the uncertainty of model simulation is estimated by the generalised likelihood uncertainty estimation (GLUE) methodology. The results indicate that the uncertainty of streamflow simulations, i.e., confidence interval, is lower with the new parameters estimation methodology compared to that used by original calibration methodology. The new baseflow parameters estimation framework could be applied in VIC model and other appropriate models for PUB.

Condition monitoring based on modified CUSUM and EWMA control charts

2018 ◽  
Vol 24 (1) ◽  
pp. 119-132 ◽  
Author(s):  
Suzana Paula Gomes Fernando da Silva Lampreia ◽  
José Fernando Gomes Requeijo ◽  
José António Mendonça Dias ◽  
Valter Martins Vairinhos ◽  
Patrícia Isabel Soares Barbosa
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Failure Detection ◽  
Parameters Estimation ◽  
False Alarms ◽  
Model Parameters ◽  
Content Type ◽  
Catastrophic Failures ◽  
The Right ◽  
Weighted Moving Average

Purpose The application of condition-based maintenance on selected equipment can allow online monitoring using fixed, half-fixed or portable sensors. The collected data not always allow a straightforward interpretation and many false alarms can happen. The paper aims to discuss these issues. Design/methodology/approach Statistical techniques can be used to perform early failure detection. With the application of Cumulative Sum (CUSUM) Modified Charts and the Exponentially Weighted Moving Average (EWMA) Charts, special causes of variation can be detected online and during the equipment functioning. Before applying these methods, it is important to check data for independence. When the independence condition is not verified, data should be modeled with an ARIMA (p, d, q) model. Parameters estimation is obtained using the Shewhart Traditional Charts. Findings With data monitoring and statistical methods, it is possible to detect any system or equipment failure trend, so that we can act at the right time to avoid catastrophic failures. Originality/value In this work, an electro pump condition is monitored. Through this process, an anomaly and four stages of aggravation are forced, and the CUSUM and EWMA modified control charts are applied to test an online equipment monitoring. When the detection occurs, the methodology will have rules to define the degree of intervention.

scholarly journals Analytical Approximations of Long-Exposure Point Spread Functions and their Use

1989 ◽  
Vol 8 ◽  
pp. 657-661
Author(s):  
O. Bendinelli ◽  
G. Parmeggiani ◽  
F. Zavatti
Keyword(s):  
Gaussian Model ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Point Spread Functions ◽  
Spherical Source ◽  
Analytical Approximations ◽  
Correct Model ◽  
Point Spread ◽  
Surface Image ◽  
Star Images

AbstractThe observed light distribution in long exposure star images (PSF) may be fitted equally well by a variety of models. But dealing with undersainpled star images, only the use of the multi-Gaussian model allows the correct model parameters estimation, taking into account integration on pixel surface, image off-centering and background behaviour. It is also shown that the convolution of a spherical source with the multi-Gaussian and Moffat’s models gives in practice the same result.

scholarly journals Regional Parameter Estimation of the SWAT Model: Methodology and Application to River Basins in the Peruvian Pacific Drainage

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3198
Author(s):  
Flavio Alexander Asurza-Véliz ◽  
Waldo Sven Lavado-Casimiro
Keyword(s):  
Water Balance ◽  
Assessment Tool ◽  
Hydrological Cycle ◽  
Swat Model ◽  
Parameters Estimation ◽  
Data Availability ◽  
Model Parameters ◽  
Hydrological Data ◽  
The Pacific ◽  
Main Components

This study presents a methodology for the regional parameters estimation of the SWAT (Soil and Water Assessment Tool) model, with the objective of estimating daily flow series in the Pacific drainage under the context of limited hydrological data availability. This methodology has been designed to obtain the model parameters from a limited number of basins (14) to finally regionalize them to basins without hydrological data based on physical-climatic characteristics. In addition, the bootstrapping method was selected to estimate the uncertainty associated with the parameters set selection in the regionalization process. In general, the regionalized parameters reduce the initial underestimation which is reflected in a better quantification of daily flows, and improve the low flows performance. Furthermore, the results show that the SWAT model correctly represents the water balance and seasonality of the hydrological cycle main components. However, the model does not correctly quantify the high flows rates during wet periods. These findings provide supporting information for studies of water balance and water management on the Peruvian Pacific drainage. The approach and methods developed can be replicated in any other region of Peru.

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