Reliability Engineering International Research Articles

Comprehensive Scientometric Analysis and Longitudinal SDG Mapping of Quality and Reliability Engineering International Journal

Comprehensive Scientometric Analysis and Longitudinal SDG Mapping of Quality and Reliability Engineering International Journal

The ENBIS‐21 quality and reliability engineering international special issue

The ENBIS-21 Quality and Reliability Engineering International Special Issue is related to the contributions to the 21st Annual Conference of the European Network for Business and Industrial Statistics (ENBIS) held online from 13 to 15 September, 2021. The topics covered by the 12 papers included in this special issue fall within the scope of ENBIS, whose ultimate goal is to bridge the gap between methodology and application in the field of business and industrial statistics and foster networking between statisticians in academic and commercial settings. It becomes evident from the papers on this issue that classical methods of industrial statistics are still having a wide range of applications and expansions towards complex and big data structures as well as they are increasingly cross-fertilized by artificial intelligence and machine learning perspectives and approaches. The issue is opened by the contribution of Yang et al.1 in the Statistical Process Monitoring (SPM) of data in the form of tensors. The authors develop a hierarchical in-situ process monitoring approach for anomaly detection, which is applied to thermal video streams acquired during a metal additive manufacturing process for highly complex parts. A more traditional SPM approach based on generalized likelihood ratio (GLR) control charts is elaborated by Rizzo and Di Bucchianico2 to set up tailor-made monitoring for high-purity (high-quality) processes and deals with issues arising in GLR control charts for discrete processes. Along the line of developing statistical methods for complex data structures, Gril et al.3 set up a tensor-on-tensor regression model able to dynamically predict repetitive human motions helpful in avoiding unwanted collisions at the interface with robots in industrial assembly tasks. Chuquin et al.4 present a straightforward extension of the K-means clustering to spatially correlated functional data, with an application in the analysis of the normalized difference vegetation index in a large region of Ecuador. From a more general perspective, Vanacore et al.5 address the problem of assessing prediction performance in the multi-class classification of imbalanced data sets. In the optimal Design of Experiments (DoE) Pesce et al.6 offer new results that are oriented to bias reduction in large datasets and protection against confounders. Whereas, Kirchhoff et al.7 use DoE as the initial solution for the optimization of shift planning in high-bay warehouse operations based on Gaussian process models with low-rank correlation kernels. Risk assessment and management is the topic of the following three papers. Testik and Unlu8 compare traditional and fuzzy failure modes and effect analysis to rank risks in different areas of testing and calibration laboratories. Meynaoui et al.9 deal with an accidental scenario in a sodium-cooled fast nuclear reactor by means of a global sensitivity analysis of the input parameter distribution used in numerical simulator outputs to model physical phenomena. Lai and Zwetsloot10 offer a data-driven ensemble ranking system of product quality, which is validated in the identification of high-risk manufacturers in a solar industry case study. The final two papers are devoted to the Reliability and Maintenance of repairable systems. El-Aroui and Gaudoin11 propose a general goodness-of-fit test for imperfect repair models that has the favorable property of being asymptotically distribution-free for renewal and power law processes. Fouladirad et al.12 develop a new bounded transformed gamma process model to describe and predict degradation phenomena by relaxing the typical assumption that the degradation level can grow indefinitely, which is often unrealistic for technological units. The proposed model is also applied to a set of wear measures of cylinder liners that equip a diesel engine for marine propulsion. We are deeply grateful to the journal staff for their professional assistance in the editorial process, as well as to the anonymous referees for the time and effort spent to provide careful and constructive comments, and to all the authors, who made revisions on a very tight timescale.

Editorial

AbstractQuality and Reliability Engineering International, Vol. 38, No. 1

A note on ‘The exponentiated power Lomax distribution and its applications'

AbstractAbd El‐Monsef, Sweilam and Sabry [Quality and Reliability Engineering International, 2020, doi: 10.1002/qre.2780] proposed a four‐parameter exponentiated power Lomax distribution, derived its properties and illustrated three data applications. In this note, we show that several properties expressed in terms of infinite sums can be simplified to a known special function. We also show that certain one‐parameter distributions can provide better fits to at least two of the data sets considered.

On the omega probability distribution

AbstractDombi, Jónás, Tóth, and Árva (Quality and Reliability Engineering International, 2018, doi: 10.1002/qre.2425) introduced the omega probability distribution. They stated that main properties of this distribution are “difficult to deal with” analytically. In this note, we derive closed‐form expressions for all of the main properties of the omega distribution. A real data illustration of the derived expressions is also given.

A note on “Parameter estimation for bivariate Weibull distribution using generalized moment method for reliability evaluation”

AbstractYuan [Quality and Reliability Engineering International, 2018, doi: 10.1002/qre.2276] proposed a generalized moment method for parameter estimation of a particular bivariate Weibull distribution and derived corresponding confidence intervals. The expressions given for the confidence intervals do not appear correct. In this note, we correct the confidence intervals and recompute them for two real data sets.

The ENBIS‐17 Quality and Reliability Engineering International Special Issue

The ENBIS‐17 Quality and Reliability Engineering International Special Issue

Bibliometric analysis for process capability research

Bibliometric research focuses on the analysis of the bibliographic indicators quantitatively. It is a useful way for classifying information according to different variables, including journals, institutions and countries. This paper presents a general overview of research conducted in the field of process capability using bibliometric indicators. The main advantage of the current study is that the bibliometric indicators provide a broad picture and identify some of the most influential research conducted in the area of process capability. The analysis is divided into key sections focused on relevant journals, research papers, authors, institutions and countries. Web of Science (WOS) databases is the source of data for carrying out this bibliometric analysis. The study reveals that W.L. Pearn and C.W. Wu. are the two most influential authors in process capability research. On the other hand, Journal of Quality Technology, and Quality and Reliability Engineering International are the two most influential journals for publishing process capability researches. Furthermore, Taiwan is found to be the most influential country, followed by the U.S.A. in the process capability research.

Exploring the Use of Design of Experiments in Industrial Processes Operating Under Closed‐Loop Control

Industrial manufacturing processes often operate under closed‐loop control, where automation aims to keep important process variables at their set‐points. In process industries such as pulp, paper, chemical and steel plants, it is often hard to find production processes operating in open loop. Instead, closed‐loop control systems will actively attempt to minimize the impact of process disturbances. However, we argue that an implicit assumption in most experimental investigations is that the studied system is open loop, allowing the experimental factors to freely affect the important system responses. This scenario is typically not found in process industries. The purpose of this article is therefore to explore issues of experimental design and analysis in processes operating under closed‐loop control and to illustrate how Design of Experiments can help in improving and optimizing such processes. The Tennessee Eastman challenge process simulator is used as a test‐bed to highlight two experimental scenarios. The first scenario explores the impact of experimental factors that may be considered as disturbances in the closed‐loop system. The second scenario exemplifies a screening design using the set‐points of controllers as experimental factors. We provide examples of how to analyze the two scenarios. © 2017 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd

Special Issue on INFORMS 2015 Annual Meeting

Special Issue on INFORMS 2015 Annual Meeting

A Multivariate Control Chart for Autocorrelated Tool Wear Processes

Full automation of metal cutting processes has been a long held goal of the manufacturing industry. One key obstacle to achieving this ambition has been the inability to monitor completely the condition of the cutting tool in real time, as premature tool breakage and heavy tool wear can result in substantial costs through damage to the machinery and increasing the risk of non‐conforming items that have to be scrapped or reworked. Instead, the condition of the tool has to be indirectly monitored using modern sensor technology that measures the acoustic emission, sound, spindle power and vibration of the tool during a cut. An online monitoring procedure for such data is proposed. Firstly, the standard deviation is extracted from each sensor signal to summarise the state of the tool after each cut. Secondly, a multivariate autoregressive state space model is specified for estimating the joint effects and cross‐correlation of the sensor variables in Phase I. Then we apply a distribution‐free monitoring scheme to the model residuals in Phase II, based on binomial type statistics. The proposed methodology is illustrated using a case study of titanium alloy milling (a machining process used in the manufacture of aircraft landing gears) from the Advanced Manufacturing Research Centre in Sheffield, UK, and is demonstrated to outperform alternative residual control charts in this application. © 2016 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.

The ENBIS‐15 Quality and Reliability Engineering International Special Issue

The ENBIS‐15 Quality and Reliability Engineering International Special Issue

The ENBIS‐14 Quality and Reliability Engineering International Special Issue

The ENBIS‐14 Quality and Reliability Engineering International Special Issue

Impact of Autocorrelation on Principal Components and Their Use in Statistical Process Control

A basic assumption when using principal component analysis (PCA) for inferential purposes, such as in statistical process control (SPC), is that the data are independent in time. In many industrial processes, frequent sampling and process dynamics make this assumption unrealistic rendering sampled data autocorrelated (serially dependent). PCA can be used to reduce data dimensionality and to simplify multivariate SPC. Although there have been some attempts in the literature to deal with autocorrelated data in PCA, we argue that the impact of autocorrelation on PCA and PCA-based SPC is neither well understood nor properly documented. This article illustrates through simulations the impact of autocorrelation on the descriptive ability of PCA and on the monitoring performance using PCA-based SPC when autocorrelation is ignored. In the simulations, cross-correlated and autocorrelated data are generated using a stationary first-order vector autoregressive model. The results show that the descriptive ability of PCA may be seriously affected by autocorrelation causing a need to incorporate additional principal components to maintain the model's explanatory ability. When all variables have equal coefficients in a diagonal autoregressive coefficient matrix, the descriptive ability is intact, while a significant impact occurs when the variables have different degrees of autocorrelation. We also illustrate that autocorrelation may impact PCA-based SPC and cause lower false alarm rates and delayed shift detection, especially for negative autocorrelation. However, for larger shifts, the impact of autocorrelation seems rather small. © 2015 The Authors. Quality and Reliability Engineering International published by John Wiley & Sons Ltd.

Recent Advances and Future Directions for Quality Engineering

The origins of quality engineering are in manufacturing, where quality engineers apply basic statistical methodologies to improve the quality and productivity of products and processes. In the past decade, people have discovered that these methodologies are effective for improving almost any type of system or process, such as financial, health care, and supply chains.This paper begins with a review of key advances and trends within quality engineering over the past decade. The second part uses the first part as a foundation to outline new application areas for the field. It also discusses how quality engineering needs to evolve in order to make significant contributions to these new areas. © 2015 The Authors Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.

The Effect of Autocorrelation on the Hotelling T2 Control Chart

One of the basic assumptions for traditional univariate and multivariate control charts is that the data are independent in time. For the latter, in many cases, the data are serially dependent (autocorrelated) and cross‐correlated because of, for example, frequent sampling and process dynamics. It is well known that the autocorrelation affects the false alarm rate and the shift‐detection ability of the traditional univariate control charts. However, how the false alarm rate and the shift‐detection ability of the Hotelling T2 control chart are affected by various autocorrelation and cross‐correlation structures for different magnitudes of shifts in the process mean is not fully explored in the literature. In this article, the performance of the Hotelling T2 control chart for different shift sizes and various autocorrelation and cross‐correlation structures are compared based on the average run length using simulated data. Three different approaches in constructing the Hotelling T2 chart are studied for two different estimates of the covariance matrix: (i) ignoring the autocorrelation and using the raw data with theoretical upper control limits; (ii) ignoring the autocorrelation and using the raw data with adjusted control limits calculated through Monte Carlo simulations; and (iii) constructing the control chart for the residuals from a multivariate time series model fitted to the raw data. To limit the complexity, we use a first‐order vector autoregressive process and focus mainly on bivariate data. © 2014 The Authors. Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.

The ENBIS‐13 Quality and Reliability Engineering International Special Issue

The ENBIS‐13 Quality and Reliability Engineering International Special Issue

Data Analysis for Condition‐Based Railway Infrastructure Maintenance

Condition assessment is crucial to optimize condition-based maintenance actions of assets such as railway infrastructure, where a faulty state might have severe consequences. Hence, railways are regularly inspected to detect failure events and prevent the inspected item (e.g. rail) to reach a faulty state with potentially safety critical consequences (e.g. derailment). However, the preventive measures (e.g. condition-based maintenance) initiated by the inspection results may cause traffic disturbances, especially if the expected time to a faulty state is short. The alarm limits are traditionally safety related and often based on geometrical properties of the inspected item. Maintenance limits would reduce the level of emergency, producing earlier alarms and increasing possibilities of planned preventive rather than acute maintenance. However, selecting these earlier maintenance limits in a systematic way while balancing the risk of undetected safety-critical faults and false alarms is challenging. Here, we propose a statistically based approach using condition data of linear railway infrastructure assets. The data were obtained from regular inspections done by a railway track measurement wagon. The condition data were analysed by a control chart approach to evaluate the possibility for earlier detection of derailment hazardous faults using both temporal and spatial information. The study indicates that that the proposed approach could be used for condition assessment of tracks. Control charts led to earlier fault warnings compared to the traditional approach, facilitating planned condition-based maintenance actions and thereby a reduction of track downtime. Copyright © 2014 The Authors. Quality and Reliability Engineering International published by John Wiley & Sons, Ltd.

Enhancing the Performance of Exponentially Weighted Moving Average Charts: Discussion

Abbas et al. (Abbas N, Riaz M, Does RJMM. Enhancing the performance of EWMA charts. Quality and Reliability Engineering International 2011; 27(6):821–833) proposed the use of signaling schemes with exponentially weighted moving average charts (named as 2/2 and modified − 2/3 schemes) for their improved design structures. A two‐sided control structure of these schemes is given in the paper. The computational results in some of the tables of that paper for modified − 2/3 are mistakenly given for the one‐sided control structure. The corrected two‐sided results are provided here. It is noticed that the superiority of the proposed scheme over the classical exponentially weighted moving average chart remains but is less pronounced. Copyright © 2014 John Wiley & Sons, Ltd.

Data Mining—A Special Issue of Quality and Reliability Engineering International (QREI)

Data Mining—A Special Issue of <i>Quality and Reliability Engineering International</i> (<i>QREI</i>)