scholarly journals Reliability Estimation under Scarcity of Data: A Comparison of Three Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Leonardo Leoni ◽  
Alessandra Cantini ◽  
Farshad BahooToroody ◽  
Saeed Khalaj ◽  
Filippo De Carlo ◽  
...  
Keyword(s):  
Likelihood Estimation ◽  
Vital Role ◽  
Reliability Estimation ◽  
Least Square ◽  
Limited Data ◽  
Least Square Estimation ◽  
Process Plants ◽  
Few Data ◽  
Optimal Approach ◽  
Asset Managers

During the last decades, the optimization of the maintenance plan in process plants has lured the attention of many researchers due to its vital role in assuring the safety of operations. Within the process of scheduling maintenance activities, one of the most significant challenges is estimating the reliability of the involved systems, especially in case of data scarcity. Overestimating the average time between two consecutive failures of an individual component could compromise safety, while an underestimate leads to an increase of operational costs. Thus, a reliable tool able to determine the parameters of failure modelling with high accuracy when few data are available would be welcome. For this purpose, this paper aims at comparing the implementation of three practical estimation frameworks in case of sparse data to point out the most efficient approach. Hierarchical Bayesian modelling (HBM), maximum likelihood estimation (MLE), and least square estimation (LSE) are applied on data generated by a simulated stochastic process of a natural gas regulating and metering station (NGRMS), which was adopted as a case of study. The results identify the Bayesian methodology as the most accurate for predicting the failure rate of the considered devices, especially for the equipment characterized by less data available. The outcomes of this research will assist maintenance engineers and asset managers in choosing the optimal approach to conduct reliability analysis either when sufficient data or limited data are observed.

Mathematical properties of the Kumaraswamy-Lindley distribution and its applications

2017 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Hamdy Salem ◽  
Abd-Elwahab Hagag
Keyword(s):  
Hazard Function ◽  
Likelihood Estimation ◽  
Real Data ◽  
Quantile Function ◽  
Moment Generating Function ◽  
Least Square ◽  
Estimation Of Parameters ◽  
Least Square Estimation ◽  
Mathematical Properties ◽  
Composite Distribution

In this paper, a composite distribution of Kumaraswamy and Lindley distributions namely, Kumaraswamy-Lindley Kum-L distribution is introduced and studied. The Kum-L distribution generalizes sub-models for some widely known distributions. Some mathematical properties of the Kum-L such as hazard function, quantile function, moments, moment generating function and order statistics are obtained. Estimation of parameters for the Kum-L using maximum likelihood estimation and least square estimation techniques are provided. To illustrate the usefulness of the proposed distribution, simulation study and real data example are used.

Key Subsystem Identification of the CNC Machine Tools

2013 ◽  
Vol 329 ◽  
pp. 157-162 ◽  
Author(s):  
Xiao Cui Zhu ◽  
Fei Chen ◽  
Xiao Bing Li ◽  
Zhao Jun Yang ◽  
Ying Nan Kan ◽  
...  
Keyword(s):  
Machine Tools ◽  
Probability Function ◽  
Likelihood Estimation ◽  
Least Square ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Least Square Estimation ◽  
Failure Data ◽  
Novel Method

In order to find the key subsystems which affect the reliability of the CNC machine tools is an important and yet harder problem due to the lack of failure data and statistical analysis. A novel method based on posterior probability function for identifying the key subsystems of the CNC machine tools was proposed. Firstly, subsystem reliability of the CNC machine tools was estimated using the John estimator, since the failure data were censored. Secondly, parametric fitting was conduct with Weibull distribution using the maximum likelihood estimation (MLE) or the least square estimation (LSE). Thirdly, the probability function of each subsystems were attained by the reliability modes of the systems and the whole system. Finally, a case study was given, which proved that the proposed method could find out the key subsystems rapidly and accurately.

scholarly journals Turning on the Lights: A Meta-Analysis of Residential Electricity Demand Elasticities

2004 ◽  
Vol 36 (1) ◽  
pp. 65-81 ◽  
Author(s):  
James A. Espey ◽  
Molly Espey
Keyword(s):  
Meta Analysis ◽  
Likelihood Estimation ◽  
Electricity Demand ◽  
Least Square ◽  
Least Square Estimation ◽  
Policy Makers ◽  
Income Elasticities ◽  
Price And Income Elasticities ◽  
Residential Electricity ◽  
Residential Demand

Meta-analysis is used to quantitatively summarize previous studies of residential electricity demand to determine if there are factors that systematically affect estimated elasticities. In this study, price and income elasticities of residential demand for electricity from previous studies are used as the dependent variables, with data characteristics, model structure, and estimation technique as independent variables, using both least square estimation of a semilog model and maximum likelihood estimation of a gamma model. The findings of this research can help better inform public policy makers, regulators, and utilities about the responsiveness of residential electricity consumers to price and income changes.

scholarly journals Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice

2021 ◽  
Vol 18 (7) ◽  
pp. 3349
Author(s):  
Leonardo Leoni ◽  
Farshad BahooToroody ◽  
Saeed Khalaj ◽  
Filippo De Carlo ◽  
Ahmad BahooToroody ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Likelihood Estimation ◽  
Reliability Estimation ◽  
Least Square ◽  
Mathematical Representation ◽  
Failure Behavior ◽  
Reliability Engineering ◽  
Prior Beliefs ◽  
Three Samples ◽  
Integrity Management

Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process.

scholarly journals Odd Inverse Power Generalized Weibull Generated Family of Distributions: Properties and Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
A.S. Al-Moisheer ◽  
I. Elbatal ◽  
Waleed Almutiry ◽  
Mohammed Elgarhy
Keyword(s):  
Likelihood Estimation ◽  
Exponential Model ◽  
Rate Function ◽  
Least Square ◽  
Inverse Power ◽  
Real World Data ◽  
Least Square Estimation ◽  
New Family ◽  
New Models ◽  
Generated Family

A novel family of produced distributions, odd inverse power generalized Weibull generated distributions, is introduced. Various mathematics structural properties for the odd inverse power generalized Weibull generated family are computed. Numerical analysis for mean, variance, skewness, and kurtosis is performed. The new family contains many new models, and the densities of the new models can be right skewed and symmetric with “unimodal” and “bimodal” shapes. Also, its hazard rate function can be “constant,” “decreasing,” “increasing,” “increasing-constant,” “upside-down-constant,” and “decreasing-constant.” Different types of entropies are calculated. Some numerical values of various entropies for some selected values of parameters for the odd inverse power generalized Weibull exponential model are computed. The maximum likelihood estimation, least square estimation, and weighted least square estimation approaches are used to estimate the OIPGW-G parameters. Many bivariate and multivariate type models have been also derived. Two real-world data sets are used to demonstrate the new family’s use and versatility.

scholarly journals Reliability Estimation of Reinforced Slopes to Prioritize Maintenance Actions

2021 ◽  
Vol 18 (2) ◽  
pp. 373
Author(s):  
Farshad BahooToroody ◽  
Saeed Khalaj ◽  
Leonardo Leoni ◽  
Filippo De Carlo ◽  
Gianpaolo Di Bona ◽  
...  
Keyword(s):  
Urban Areas ◽  
Evaluation Method ◽  
Likelihood Function ◽  
Reliability Estimation ◽  
Analytical Models ◽  
Geotechnical Structures ◽  
Reinforced Slopes ◽  
The Stability ◽  
Methods Analytical ◽  
Asset Managers

Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8×10−5 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods.

High-Accuracy Fundamental Frequency Detection in Power System

2011 ◽  
Vol 383-390 ◽  
pp. 4962-4966
Author(s):  
Ling Li ◽  
Guo Bin Jin ◽  
Shao Ping Huang ◽  
Xiao Peng
Keyword(s):  
Power System ◽  
Fundamental Frequency ◽  
Frequency Measurement ◽  
Least Square ◽  
Signal Model ◽  
Least Square Estimation ◽  
Signal Process ◽  
Frequency Detection ◽  
Novel Method ◽  
Subspace Estimation

A novel method on frequency measurement based on improved TLS-ESPRIT (total least square estimation of signal parameters via rotational invariance techniques) is proposed in this paper with the research on fundamental frequency measurement in power system. TLS-ESPRIT is belong to subspace estimation in modern signal process. Noise is included in signal model, so it is independent on noise. But the same multi-poles cannot be taken when signal is in noise and based on TLS-ESPRIT. Multiple poles restoring is presented to take the true poles accurately. It is revealed that fundamental frequency is detected accurately in harmonics, interharmonics, noise and frequency fluctuations and better anti-noise ability in particular better adaptiveness on time varying signal in amplitude by simulation results.

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