Novel Approaches to Prediction of a Future Number of Failures Based on Previous In-Service Inspections

2018 ◽  
pp. 225-248
Author(s):  
Nicholas A. Nechval ◽  
Konstantin N. Nechval
Keyword(s):  
Performance Index ◽  
Failure Time ◽  
Parametric Uncertainty ◽  
Statistical Problem ◽  
Unknown Parameters ◽  
Invariant Embedding ◽  
Prediction Limits ◽  
Novel Approaches ◽  
Special Case ◽  
Two Parameter

In this chapter, we present novel approaches to predictions of the number of failures that will be observed in a future inspection of a sample of units, based only on the results of the previous in-service inspections of the same sample. The failure-time of such units is modeled with a distribution from a two-parameter Weibull distribution. The different cases of parametric uncertainty are considered. The pivotal quantity averaging approach proposed here for constructing point prediction and simple prediction limits emphasizes pivotal quantities relevant for eliminating unknown parameters from the problems and represents a special case of the method of invariant embedding of sample statistics into a performance index applicable whenever the statistical problem is invariant under a group of transformations, which acts transitively on the parameter space. For illustration, a numerical example is given.

Effective Optimization of Statistical Decisions for Age Replacement Problems under Parametric Uncertainty

2017 ◽  
pp. 1-16
Author(s):  
N. A. Nechval ◽  
K. N. Nechval
Keyword(s):  
Original Problem ◽  
Decision Rules ◽  
Parametric Uncertainty ◽  
Statistical Technique ◽  
Unknown Parameters ◽  
Sample Statistic ◽  
Invariant Embedding ◽  
Replacement Model ◽  
Statistical Decisions ◽  
Age Replacement

In this chapter, an innovative model for age replacement is proposed. The costs included in the age replacement model are not assumed to be constants. For effective optimization of statistical decisions for age replacement problems under parametric uncertainty, based on a past random sample of lifetimes, the pivotal quantity averaging (PQA) approach is suggested. The PQA approach represents a simple and computationally attractive statistical technique. In this case, the transition from the original problem to the equivalent transformed problem (in terms of pivotal quantities and ancillary factors) is carried out via invariant embedding a sample statistic in the original problem. The approach allows one to eliminate unknown parameters from the problem and to find the better decision rules, which have smaller risk than any of the well-known decision rules. Unlike the Bayesian approach, the proposed approach is independent of the choice of priors. For illustration, numerical examples are given.

Intelligent Constructing Exact Tolerance Limits for Prediction of Future Outcomes Under Parametric Uncertainty

2021 ◽  
pp. 701-729
Author(s):  
Nicholas A. Nechval
Keyword(s):  
Parametric Uncertainty ◽  
Statistical Problem ◽  
Tolerance Limits ◽  
Novel Technique ◽  
Simulation Monte Carlo ◽  
Statistical Tolerance ◽  
Future Outcomes ◽  
Tolerance Factors ◽  
Two Parameter ◽  
Future Sample

The problem of constructing one-sided exact statistical tolerance limits on the kth order statistic in a future sample of m observations from a distribution of log-location-scale family on the basis of an observed sample from the same distribution is considered. The new technique proposed here emphasizes pivotal quantities relevant for obtaining tolerance factors and is applicable whenever the statistical problem is invariant under a group of transformations that acts transitively on the parameter space. The exact tolerance limits on order statistics associated with sampling from underlying distributions can be found easily and quickly making tables, simulation, Monte Carlo estimated percentiles, special computer programs, and approximation unnecessary. Finally, numerical examples are given, where the tolerance limits obtained by using the known methods are compared with the results obtained through the proposed novel technique, which is illustrated in terms of the extreme-value and two-parameter Weibull distributions.

scholarly journals Inference of stress-strength reliability for two-parameter of exponentiated Gumbel distribution based on lower record values

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249028
Author(s):  
Ehsan Fayyazishishavan ◽  
Serpil Kılıç Depren
Keyword(s):  
Information Matrix ◽  
Gumbel Distribution ◽  
Sampling Technique ◽  
Real Data ◽  
Record Values ◽  
Unknown Parameters ◽  
Data Set ◽  
Credible Intervals ◽  
Lower Records ◽  
Two Parameter

The two-parameter of exponentiated Gumbel distribution is an important lifetime distribution in survival analysis. This paper investigates the estimation of the parameters of this distribution by using lower records values. The maximum likelihood estimator (MLE) procedure of the parameters is considered, and the Fisher information matrix of the unknown parameters is used to construct asymptotic confidence intervals. Bayes estimator of the parameters and the corresponding credible intervals are obtained by using the Gibbs sampling technique. Two real data set is provided to illustrate the proposed methods.

A New Technique for Optimization of Product Acceptance Process in Terms of Misclassification Probability

2018 ◽  
pp. 1-32
Author(s):  
Nicholas A. Nechval ◽  
Konstantin N. Nechval
Keyword(s):  
Weibull Distribution ◽  
Optimization Technique ◽  
Parametric Uncertainty ◽  
Lifetime Distribution ◽  
Statistical Quality Control ◽  
Misclassification Probability ◽  
Distribution Parameters ◽  
A New Technique ◽  
Two Parameter

A product acceptance process is an inspecting one in statistical quality control or reliability tests, which are used to make decisions about accepting or rejecting lots of products to be submitted. This process is important for industrial and business purposes of quality management. To determine the optimal parameters of the product acceptance process under parametric uncertainty of underlying lifetime models (in terms of misclassification probability), a new optimization technique is proposed. The most popular lifetime distribution used in the field of product acceptance is a two-parameter Weibull distribution, with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Therefore, the situations are also considered when both Weibull distribution parameters are unknown. An illustrative numerical example is given.

scholarly journals A New Extended Two-Parameter Distribution: Properties, Estimation Methods, and Applications in Medicine and Geology

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1578 ◽  
Author(s):  
Hazem Al-Mofleh ◽  
Ahmed Z. Afify ◽  
Noor Akma Ibrahim
Keyword(s):  
Estimation Method ◽  
Estimation Methods ◽  
Parameter Distribution ◽  
Model Parameters ◽  
Unknown Parameters ◽  
Proposed Model ◽  
Rate Functions ◽  
The Mean ◽  
Modeling Data ◽  
Two Parameter

In this paper, a new two-parameter generalized Ramos–Louzada distribution is proposed. The proposed model provides more flexibility in modeling data with increasing, decreasing, J-shaped, and reversed-J shaped hazard rate functions. Several statistical properties of the model were derived. The unknown parameters of the new distribution were explored using eight frequentist estimation approaches. These approaches are important for developing guidelines to choose the best method of estimation for the model parameters, which would be of great interest to practitioners and applied statisticians. Detailed numerical simulations are presented to examine the bias and the mean square error of the proposed estimators. The best estimation method and ordering performance of the estimators were determined using the partial and overall ranks of all estimation methods for various parameter combinations. The performance of the proposed distribution is illustrated using two real datasets from the fields of medicine and geology, and both datasets show that the new model is more appropriate as compared to the Marshall–Olkin exponential, exponentiated exponential, beta exponential, gamma, Poisson–Lomax, Lindley geometric, generalized Lindley, and Lindley distributions, among others.

scholarly journals Estimating the Entropy for Lomax Distribution Based on Generalized Progressively Hybrid Censoring

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1219 ◽  
Author(s):  
Shuhan Liu ◽  
Wenhao Gui
Keyword(s):  
Incomplete Data ◽  
Real Data ◽  
Unknown Parameters ◽  
Data Set ◽  
Hybrid Censoring ◽  
Lomax Distribution ◽  
Bayesian Estimates ◽  
Squared Error ◽  
General Entropy Loss Function ◽  
Two Parameter

As it is often unavoidable to obtain incomplete data in life testing and survival analysis, research on censoring data is becoming increasingly popular. In this paper, the problem of estimating the entropy of a two-parameter Lomax distribution based on generalized progressively hybrid censoring is considered. The maximum likelihood estimators of the unknown parameters are derived to estimate the entropy. Further, Bayesian estimates are computed under symmetric and asymmetric loss functions, including squared error, linex, and general entropy loss function. As we cannot obtain analytical Bayesian estimates directly, the Lindley method and the Tierney and Kadane method are applied. A simulation study is conducted and a real data set is analyzed for illustrative purposes.

A Reinforcement Learning Neural Network for Robotic Manipulator Control

2018 ◽  
Vol 30 (7) ◽  
pp. 1983-2004 ◽  
Author(s):  
Yazhou Hu ◽  
Bailu Si
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Performance Index ◽  
Robotic Manipulator ◽  
The State ◽  
Unknown Parameters ◽  
Proposed Model ◽  
Action Network ◽  
Action Policy ◽  
The Stability

We propose a neural network model for reinforcement learning to control a robotic manipulator with unknown parameters and dead zones. The model is composed of three networks. The state of the robotic manipulator is predicted by the state network of the model, the action policy is learned by the action network, and the performance index of the action policy is estimated by a critic network. The three networks work together to optimize the performance index based on the reinforcement learning control scheme. The convergence of the learning methods is analyzed. Application of the proposed model on a simulated two-link robotic manipulator demonstrates the effectiveness and the stability of the model.

Adaptive Synchronized Control for a Planar Parallel Manipulator: Theory and Experiments

2005 ◽  
Vol 128 (4) ◽  
pp. 976-979 ◽  
Author(s):  
Lu Ren ◽  
James K. Mills ◽  
Dong Sun
Keyword(s):  
Parallel Manipulator ◽  
High Speed ◽  
Control Method ◽  
Parametric Uncertainty ◽  
Synchronization Error ◽  
Tracking Accuracy ◽  
Unknown Parameters ◽  
High Acceleration ◽  
Planar Parallel Manipulator ◽  
Synchronized Control

In this paper, we develop a new control method, termed adaptive synchronized (A-S) control, for improving tracking accuracy of a P-R-R type planar parallel manipulator with parametric uncertainty. The novelty of A-S control, a combination of synchronized control and adaptive control, is in the application of synchronized control to a single parallel manipulator so that tracking accuracy is improved during high-speed, high-acceleration tracking motions. Through treatment of each chain as a submanipulator; the P-R-R manipulator is thus modeled as a multi-robot system comprised of three submanipulators grasping a common payload. Considering the geometry of the platform, these submanipulators are kinematically constrained and move in a synchronous manner. To solve this synchronization control problem, a synchronization error is defined, which represents the coupling effects among the submanipulators. With the employment of this synchronization error, tracking accuracy of the platform is improved. Simultaneously, the estimated unknown parameters converge to their true values through the use of a bounded-gain-forgetting estimator. Experiments conducted on the P-R-R manipulator demonstrate the validity of the approach.

Sign in / Sign up

Export Citation Format

Share Document