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.

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

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.

scholarly journals Extended Rayleigh Distribution: Properties and Application to Failure Data

2021 ◽  
pp. 1-8
Author(s):  
Aladesuyi Alademomi ◽  
Philips Samuel Ademola ◽  
Adefolarin Adekunle David
Keyword(s):  
Real Life ◽  
Quantile Function ◽  
Rayleigh Distribution ◽  
Model Parameters ◽  
Data Set ◽  
New Model ◽  
Failure Data ◽  
Mathematical Properties ◽  
Real Life Data ◽  
Method Of Maximum Likelihood

This paper introduces a new three parameter Rayleigh distribution which generalizes the Rayleigh distribution. The new model is referred to as Extended Rayleigh (ER) distribution. Various mathematical properties of the new model including ordinary and incomplete moment, quantile function, generating function are derived. We propose the method of maximum likelihood for estimating the model parameters. A real life data set is used to compare the flexibility of the new model with other models.

scholarly journals FRI0434 PREDICTIVE FACTORS OF SERIOUS INFECTIONS IN PATIENTS WITH CHRONIC INFLAMMATORY ARTHRITIS ON TREATMENT WITH TNF INHIBITORS: REAL LIFE DATA

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 815.1-815
Author(s):  
A. Ruiz Ochoa ◽  
J. Escudero Argaluza ◽  
B. Alvarez Rodríguez ◽  
M. Vasques Rocha ◽  
C. Stoye ◽  
...  
Keyword(s):  
Inflammatory Arthritis ◽  
Associated Factors ◽  
Statistical Tests ◽  
Univariate Analysis ◽  
Real Life ◽  
Tnf Inhibitors ◽  
Charlson Index ◽  
Multivariable Logistic Regression Model ◽  
Serious Infections ◽  
Usual Clinical Practice

Background:Serious infections are one of the most feared adverse events in patients treated with biologics. To this regard data coming from randomized and long-term extension studies may not totally applied to the usual clinical practice due to the different profile of the treated patients. To study the associated factors for serious infections in patients with inflammatory arthritis treated with TNF inhibitorsObjectives:To study the associated factors for serious infections in patients with inflammatory arthritis treated with TNF inhibitorsMethods:All the medical records of the patients with inflammatory arthritis being treated with TNF inhibitors at the beginning of 2016 were retrospectively reviewed. All serious infections suffered for these patients until the end of 2018 were recorded. Serious infections were defined as those which required to admitted at the hospital for intravenous treatment. Potential variables associated with the development of these infections including: demographic and clinical characteristics, concomitant treatments or comorbidity (by Charlson index) were studied. Standard statistical tests for descriptive and univariate analyses were used and a multivariable logistic regression model was built to check independent associations.Results:Overall 334 patients (50.3% women) with a mean age of 56.67 (±12.853) were studied: 140 (41.92%) Rheumatoid arthritis (RA), 55 (16.46%) psoriatic arthritis (PsA) and 138 (41.62%) spondyloarthritis (Sp). Forty-five serious infections were observed in 30 patients, being respiratory (40%) and urinary (8.8%) the most frequent localizations. Only one patient died. By univariate analysis, disease duration, age, concomitan use of glucocorticoids (GC) (but not of synthetic DMARDs), Charlson index and specifically Diabetes Mellitus were associated with infection (p< 0.05). The type of arthritis was not associated and the results in the subset of RA patients were overall similar. In the multivariate analysis the use of GC [OR: 5.31 (1.98.14.26)] and the Charlson index [OR:2.48 (1.70;3.60)] were found to be independently associated to infection.Conclusion:In patients with inflammatory arthritis and treated with TNF inhibitors around a 10% developed any serious infection along three years of follow up. Use of GC and comorbidity emerged as the main risk factors for this complication.Disclosure of Interests:None declared

scholarly journals Testing the Validity of a Link Function Assumption in Repeated Type-II Censored General Step-Stress Experiments

Sankhya B ◽  
2021 ◽  
Author(s):  
Stefan Bedbur ◽  
Thomas Seiche
Keyword(s):  
Null Hypothesis ◽  
Statistical Tests ◽  
Operating Conditions ◽  
Link Function ◽  
Asymptotic Results ◽  
Failure Data ◽  
Stress Levels ◽  
Log Linear ◽  
Multiple Samples ◽  
General Step

AbstractIn step-stress experiments, test units are successively exposed to higher usually increasing levels of stress to cause earlier failures and to shorten the duration of the experiment. When parameters are associated with the stress levels, one problem is to estimate the parameter corresponding to normal operating conditions based on failure data obtained under higher stress levels. For this purpose, a link function connecting parameters and stress levels is usually assumed, the validity of which is often at the discretion of the experimenter. In a general step-stress model based on multiple samples of sequential order statistics, we provide exact statistical tests to decide whether the assumption of some link function is adequate. The null hypothesis of a proportional, linear, power or log-linear link function is considered in detail, and associated inferential results are stated. In any case, except for the linear link function, the test statistics derived are shown to have only one distribution under the null hypothesis, which simplifies the computation of (exact) critical values. Asymptotic results are addressed, and a power study is performed for testing on a log-linear link function. Some improvements of the tests in terms of power are discussed.

scholarly journals Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 434-455
Author(s):  
Sujan Kumar Roy ◽  
Kuldip K. Paliwal
Keyword(s):  
Kalman Filter ◽  
Speech Enhancement ◽  
Linear Prediction ◽  
Real Life ◽  
Model Parameters ◽  
Noise Variance ◽  
Noisy Speech ◽  
Kalman Gain ◽  
Whitening Filter ◽  
Prediction Coefficient

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

A robust possibilistic programming model for production-routing problem in a three-echelon supply chain

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Ali Beheshtinia ◽  
Narjes Salmabadi ◽  
Somaye Rahimi
Keyword(s):  
Supply Chain ◽  
Real Life ◽  
Model Parameters ◽  
Pickup And Delivery ◽  
Distribution Centers ◽  
Routing Problem ◽  
Remarkable Feature ◽  
Content Type ◽  
Proposed Model ◽  
Production Routing

Purpose This paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered. Design/methodology/approach At first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models. Findings The results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models. Originality/value This research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model.

Identification of ’Effective’ Linear Joints Using Coupling and Joint Identification Techniques

1998 ◽  
Vol 120 (2) ◽  
pp. 331-338 ◽  
Author(s):  
Y. Ren ◽  
C. F. Beards
Keyword(s):  
Dynamic Properties ◽  
Real Life ◽  
Model Parameters ◽  
Coupling Technique ◽  
Alternative Approach ◽  
Identification Technique ◽  
Joint Identification ◽  
Stiff Joint ◽  
Almost All ◽  
Identification Techniques

Almost all real-life structures are assembled from components connected by various types of joints. Unlike many other parts, the dynamic properties of a joint are difficult to model analytically. An alternative approach for establishing a theoretical model of a joint is to extract the model parameters from experimental data using joint identification techniques. The accuracy of the identification is significantly affected by the properties of the joints themselves. If a joint is stiff, its properties are often difficult to identify accurately. This is because the responses at both ends of the joint are linearly-dependent. To make things worse, the existence of a stiff joint can also affect the accuracy of identification of other effective joints (the term “effective joints” in this paper refers to those joints which otherwise can be identified accurately). This problem is tackled by coupling these stiff joints using a generalized coupling technique, and then the properties of the remaining joints are identified using a joint identification technique. The accuracy of the joint identification can usually be improved by using this approach. Both numerically simulated and experimental results are presented.

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