Pasquale Erto

New Practical Bayes Estimators for the 2-Parameter Weibull Distribution

New Bayes estimators for the 2-parameter Weibull model are proposed when both parameters are unknown. In many life testing situations there is prior information which can be reasonably quantified in terms of: 1) range of the shape parameter, and 2) anticipated value of a quantile (reliable life) of the sampling distribution. This paper directly incorporates such information into the estimation process, using a new (not completely specified) prior distribution. Since analytic tractability is not possible, the estimates are obtained with easy numerical integration. A Monte Carlo simulation (carried out each time on 1000 samples and also using very poor priors) has shown that these estimators are quite s-unbiased and s-efficient for a large range of parameter values of poor priors.

A New Control Chart for Weibull Technological Processes

Abstract This work provides a new Shewhart-type control chart of the Weibull percentile. The few alternative available charts are much less practical than the one proposed. The chart exploits existing estimators, known as Practical Bayes Estimators (PBE), able to integrate both technological and statistical information analytically, using the Bayes theorem. In this way the chart is able to improve the process control making use of any available information. The operative steps of the employed estimation procedure are fully explained. The performance of the chart is investigated carrying out a large Monte Carlo study. The use of the proposed chart is illustrated by means of a real applicative example.

A statistical monitoring approach for automotive on‐board diagnostic systems

The current generation of vehicle models are increasingly being equipped with on-board diagnostic (OBD) systems aimed at assessing the ‘state of health’ of important anti-pollution subsystems and components. In order to promptly diagnose and fix quality and reliability problems that may potentially affect such complex diagnostic systems, even during advanced development prior to mass production, some vehicle prototypes undergo a testing phase under realistic conditions of use (a mileage accumulation campaign). The aim of this work is to set up a statistical tool for improving the reliability of the OBD system by monitoring its operation during the mileage accumulation campaign of a new vehicle model. A dedicated software program was developed by the authors to filter the large experimental database recorded during the mileage accumulation campaign and to extract the time series of the diagnostic indices to be analysed. A model-based monitoring approach, using continuous time autoregressive (CAR) models for the time-series structure and traditional control charts for the estimated residuals, is adopted. A Kalman recursion procedure for the estimation of the unknown CAR model parameters is described. An application of the proposed approach is presented for a diagnostic index related to the state of health of the oxygen sensor. Copyright

A service quality map based on Kano's theory of attractive quality

Purpose – This paper aims to provide a quantitative decision approach to the service quality management, developed on the basis of Kanos theory of attractive quality. The proposed approach aims at exploiting contacts with service made by “mystery guests” rather than traditional surveys on customer opinions.Design/methodology/approach – A specific probabilistic model of the process of serving quality is the adopted basic tool. Multiple comparison tests aimed at controlling the service quality are the core of the proposed decision approach. In order to collect the needed sampling data, a few mystery guests who experience many customer‐service contacts are employed.Findings – A quantitative decision methodology which both allows one to evaluate the actual service quality level and provides, via comparison tests, a tool to highlight the weak and strong points of the service delivery process.Originality/value – The proposed quality map is an original graphical tool, which enables one to pin‐point strengths an...

A Procedure for Predicting and Controlling the Ship Fuel Consumption: Its Implementation and Test

In light of the International Maritime Organization guidelines, the new Regulation of the European Union urges shipping companies to set up a system for monitoring, reporting and verification of CO2 emissions based on ship fuel consumption. However, in nowadays market, there is a lack of techniques for fuel consumption monitoring that can be adopted in a real environment. The proposed procedure overcomes the use of Speed–Power curves, which are commonly utilized in the Naval Architecture, by means of a multiple linear regression model that exploits the navigation information usually available in modern ships. This procedure can be used both to alert technical management of a shipping company for anomalous fuel consumption and to quantify the fuel consumption reduction connected with any specific policy adopted to improve energy efficiency. Therefore, this can be particularly profitable for shipping companies and operators in order to claim for carbon credits. The proposed approach is currently implemented by the Energy Saving Technical Department of the Grimaldi Group on a Ro-Ro Pax ship. Copyright

A Useful Property of ``Graphical'' Estimators of Location and Scale Parameters

This paper presents a method for constructing exact s-confidence limits for reliability and/or quantiles based on graphical estimators of location and scale parameters. The method arises from the ancillary property of the reliability function estimated by using plotting positions and the least squares method. From the distribution of this estimator alone, one can obtain graphs or tables to calculate directly the s-confidence limits for both reliability and quantiles. These limits are exact even for very small sample-sizes in that they are calculated without using asymptotic approximations. An example of a graph which enables one to calculate these limits for a 2-parameter Weibull distribution, is given. This example uses plotting positions recently proposed by the author.

Wind speed parameter estimation from one-month sample via Bayesian approach

Site-specific wind potential assessment shows difficulties mainly because it needs very long on-site anemometric monitoring. This paper proposes to reduce the long-term monitoring by exploiting other initial information about parameters to be estimated via MCMC (Markov chain Monte Carlo). The proposed Bayesian approach allows the integration of prior information (e.g. obtained from atlases, databases and/or fluid-dynamic assessment) with sampling data, and furnishes effective and timely posterior information about Weibull parameters of the wind speed distribution. Real sampling data, collected from a southern Italian site, are analysed in order to illustrate the main features of the methodology. Moreover, the effectiveness of both the filtering strategy adopted to deal with the high correlation that usually characterizes the anemometric data, and the seasonal adjustment proposed to obtain a sample unbiased by seasonal effect is highlighted. The results of the application show that the proposed methodology fits the applicative needs very well. A bootstrap simulation remarks that the attained precision of the Bayesian estimates carried out from a one-month sample is comparable to the maximum likelihood estimates obtained from an actual one-year sample. Copyright

The performance of semi-empirical Bayesian control charts for monitoring Weibull data

Abstract In this paper we investigate the performance of semi-empirical Bayesian control charts to monitor the percentiles and the shape parameter of a Weibull distribution. These charts have been recently introduced in literature, where it is shown how Weibull-distributed data need specifically designed control schemes and how a Bayesian approach can help in such cases. The main focus of this paper, instead, is a simulation study of the charts’ performance. To this aim, a large Monte Carlo analysis is presented, highlighting the main effects on the chart performance of single/combined changes in the Weibull parameters. Some Weibull contour plots are presented to visualize the investigated technological scenarios. An illustrative example using a reference data-set is included too.

New Distribution-Free Plotting Position Through an Approximation to the Beta Median

Even in the modern software, graphical techniques are often utilized to visualize the data and to determine the actual underlying distribution. In this chapter, a new and better distribution-free formula is obtained via an axiomatic approach leading to a new approximation to the median of the Beta distribution. A comparative study, carried out also by using extensive Monte Carlo simulation, shows the advantages of the new solution especially in estimating the median return period and for each considered sample dimension (N = 5, 15, 30, 50).

A robust calibration methodology for an on-board diagnostic car system:

New car models are now by law equipped with on-board diagnostic (OBD) systems aimed at monitoring the state of health of strategic components that ensure low levels of polluting exhaust emissions. During development phases, for each new car model, the OBD system must be finely calibrated. This article presents a robust calibration methodology taking into account sources of variability mainly due to production process, operating, and environmental conditions. The methodology enables us to evaluate the false alarm and failure to detect risks intrinsically related to the adopted calibration. An application concerning an upstream oxygen sensor monitored by the OBD is presented.