Quality and Reliability Engineering International journal solves real-life quality and reliability problems in a broad range of engineering contexts. An integral part of the quality and reliability community, our broad topic focus attracts readers and authors from industry and academia alike. Our papers cover quality engineering challenges across electronic, electrical, mechanical, systems and software engineering, as well as operations research and management. As a quality engineering journal, we welcome papers that resolve engineering challenges by practically applying mathematical and statistical tools.

Calls for Papers

  • Call for Papers

    Degradation-based Reliability Analysis

    Submission deadline: Saturday, 31 August 2024

    Degradation data are an important source of system reliability information. With the rapid developments of sensor and information technologies, the degradation data are nowadays being extensively used in reliability analysis, including failure time distribution estimation, fault detection, remaining useful life prediction and preventive maintenance, etc. During the recent years, there has been a growing interest in developing tailored statistical models, inference procedures and optimization methods to efficiently and automatically extract reliability information buried in the degradation data and facilitate the subsequent decision-making processes.

    This special issue aims to present emerging topics in modern degradation-based reliability analysis, which addresses the original theoretical development and practical applications.

    Possible topics include but are not limited to:

    • Accelerated degradation tests and reliability analysis
    • Degradation modelling and estimation
    • Remaining useful life prediction based on degradation data
    • Maintenance optimization based on degradation data
    • Machine learning methods for analysing degradation data
    • Degradation-based operations management

    Guest Editors:

    Piao Chen
    Delft University of Technology

    Ancha Xu
    Zhejiang Gongshang University

    Zhisheng Ye
    National University of Singapore

    Keywords: Degradation modelling; Accelerated test; Remaining useful life; Preventive maintenance; Prognostic and health management; Statistical inference

    Submission Guidelines/Instructions

    Please refer to the Author Guidelines to prepare your manuscript. When submitting your manuscript, please answer the question: "Is this submission for a special issue?" by selecting the special issue title from the drop-down list.

    Submit now

  • Call for Papers

    Statistical Process Monitoring based on Machine Learning Techniques

    Submission Deadline: Wednesday, 1 January 2025

    In light of the increasing integration of Machine Learning (ML) in various industrial and medical areas, this Special Issue (SI) aims to consolidate cutting-edge research and advancements in Statistical Process Monitoring (SPM), fostering a comprehensive understanding of the field.

    The primary objective of this SI is to provide a platform for researchers, academicians, and practitioners to share their latest findings and experiences related to SPM using ML techniques. The SI will serve as a valuable resource for professionals seeking insights into novel methodologies, applications, and challenges in this rapidly evolving domain.

    With a focus on the integration of ML techniques into SPM, the SI will encompass a wide range of topics, including but not limited to anomaly detection, decision support, adaptability, pattern recognition, cross-industry applicability, efficiency, and technological innovation.

    Contributions to this special issue may include the following areas, among many others:

    • Development of innovative ML-based tools for SPM;
    • Applications of SPM in various fields, such as manufacturing, healthcare, and finance;
    • Integration of real-time data analytics and ML for continuous process improvement;
    • Case studies illustrating successful implementation of SPM in real settings;
    • Evaluation and comparison of different ML techniques for process monitoring.

    The SI allows researchers and practitioners to stay up to date with the latest advancements, methodologies, and best practices. This is crucial for fostering innovation and improving the effectiveness of SPM techniques. Thus, the SI seeks to foster collaboration and knowledge exchange, pushing the boundaries of what is currently possible in the field of SPM.

    We invite submissions of original research papers, review articles, and case studies that showcase novel approaches, methodologies, and practical implementations of ML methods in SPM. By shedding light on the SOTA techniques and their real-world impact, this SI aims to advance the field of SPM by contributing to the ongoing evolution of ML-based SPM.

    Guest Editors:

    Dr. Arne Johannssen
    University of Hamburg

    Dr. Peihua Qiu
    University of Florida

    Dr. Xiulin Xie
    Florida State University

    Dr. Ali Yeganeh
    University of Hamburg

    Keywords: Statistical Process Monitoring, Machine Learning, Artificial Intelligence, Anomaly Detection, Predictive Maintenance, Industrial Automation, Real-time Monitoring, Pattern Recognition, Process Control, Decision Support Systems.

    Submission Guidelines/Instructions

    Please refer to the Author Guidelines to prepare your manuscript. When submitting your manuscript, please answer the question: "Is this submission for a special issue?" by selecting the special issue title from the drop-down list.

    Important Dates:

    Submission Deadline: Wednesday, 1 January 2025
    Final Decisions by: Friday, 1 August 2025

    Submit now


Recent issues

A new partnership with Engineering Reports

Quality and Reliability Engineering International supports Engineering Reports, a new Wiley Open Access journal dedicated to all areas of engineering and computer science.

With a broad scope, the journal is meant to provide a unified and reputable outlet for rigorously peer-reviewed and well-conducted scientific research. See the full Aims & Scope here. Articles published by Engineering Reports are fully open access: immediately freely available to read, download and share. Your fees may be covered. Find out more here.