LAB NOTICE
- [VSI][Reliability Engineering & System Safety] Smart Maintenance Synergy: AI-Driven Diagnosis, Prognosis, and Decision Automation Submission deadline: 01 June 2026
- [VSI][Reliability Engineering & System Safety] Smart Maintenance Synergy: AI-Driven Diagnosis, Prognosis, and Decision AutomationSubmission deadline: 01 June 2026 Reliability Engineering & System Safety | ScienceDirect.com by Elsevier - Reliability Engineering & System Safety | ScienceDirect.com by ElsevierThe complexity and interconnectivity of contemporary industrial systems, including energy systems, aerospace equipment, and intelligent manufacturing production lines, are increasing continuously. Under such circumstances, traditional operation and maintenance models are confronted with severe challenges. For example, it is difficult to integrate multi-source heterogeneous data (sensors, logs, images), resulting in fragmented diagnosis and prediction; most artificial intelligence (AI) models only provide fault warnings and lack dynamic optimization decision-making capabilities; fixed rule bases or static models fail under sudden working conditions (such as extreme weather and load mutations), etc. This special issue focuses on a “collaborative intelligent operation and maintenance” framework, which aims to break the chain of diagnosis-prediction-maintenance through multimodal AI fusion, cross-system knowledge transfer, and autonomous decision-making closed loops, and promote the evolution of operation and maintenance from “single point intelligence” to “global collaboration”, and ultimately achieve the joint optimization of dynamic reliability and resource efficiency.Guest editors:Prof. Baoping CaiCollege of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, ChinaProf. Yiliu LiuDepartment of Mechanical and Industrial Engineering, Norwegian University of Technology , Trondheim, NorwayProf. Salim AhmedFaculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John's, CANADADr. Xiaoyan ShaoCollege of Mechanical and Electrical Engineering, China University of Petroleum (East China) , Qingdao, ChinaSpecial issue information:Full scope of the Special Issue(1) Integration of multimodal AI for diagnosis and prognosis using diverse data sources to enhance system health understanding.(2) Cross-system knowledge transfer strategies to apply insights from one context to improve maintenance practices in interconnected systems.(3) Autonomous decision-making frameworks that utilize AI for dynamic optimization of maintenance strategies in real-time.(4) Fault diagnosis and prognosis approaches that leverage causal modeling and AI for proactive maintenance solutions.(5) Machine learning and causal inference integration to address complex reliability and safety challenges in data-rich environments.(6) Validation and verification techniques for AI-driven models to ensure practical reliability in industrial applications.(7) Smart maintenance in new applications, such as renewable energy industries, smart grid field, etc.Manuscript submission information:Submission InformationManuscript submission open date: 1 July 2025Manuscript submission deadline: 1 June 2026You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Dr. Xiaoyan Shao.Please refer to the Guide for Authors to prepare your manuscript, and select the article type of "VSI: Smart Maintenance Synergy" when submitting your manuscript online at the journal’s submission platform Editorial Manager®. Both the Guide for Authors and the submission portal could also be found on the Journal Homepage.Keywords:Reliability, Artificial intelligence, Diagnosis, Prognosis, Decision-making
2026-04-20
- [VSI][Reliability Engineering & System Safety]Advanced resilience assessment & enhancement for complex engineering systems exposed to multi-hazards
- [VSI][Reliability Engineering & System Safety] Advanced resilience assessment & enhancement for complex engineering systems exposed to multi-hazardsSubmission deadline: 28 February 2027 Reliability Engineering & System Safety | ScienceDirect.com by Elsevier - Reliability Engineering & System Safety | ScienceDirect.com by ElsevierReliability Engineering & System Safety | Journal | ScienceDirect.com by Elsevier27 February 2026Advanced resilience assessment & enhancement for complex engineering systems exposed to multi-hazardsSubmission deadline: 28 February 2027 In the context of the intensifying repercussions of global climate change and increasing industrialization worldwide, complex engineering systems (CESs) are ever more exposed to challenges of multi-hazard scenarios that are characterized by simultaneous, cascading, or compounded threats deriving from natural disasters and anthropogenic hazards. The interactions of sub-systems may amplify cross-scale failure propagation, demanding higher resilience levels in multi-hazard scenarios. Therefore, it is critical to quantify and enhance system resilience in such destructive scenarios.Traditional methods are inadequate to address multi-hazard scenarios. Moreover, the complexity of modern CESs coupled with climate-driven volatility and evolving technological threats, requires advanced and accurate resilience analytical methods to track system performance over time. Advanced tools and smart technology could be used to characterize system performance. The resilience results could highlight the fluctuations in system performance exposed to multi-hazard disruptions, further optimizing the allocation of resilience enhancement measures.This special issue focuses on the state-of-the-art in reliability and resilience modeling for CESs under multi-hazard conditions. We aim to address critical gaps including: 1) Cross-scale modeling and high-fidelity failure analysis of CESs in multi-hazard scenarios; 2) Dynamic system performance assessment frameworks integrating real-time monitoring and AI-based prediction; 3) Reliability and resilience quantification for CESs under cascading disruptive events; 4) Industrial applications of resilience optimization algorithms to withstand multi-hazards; and 5) Effects of climate change on reliability and resilience of CESs and adaptation strategies. Contributions addressing the specific framework of Natech events and multi-hazards involving the impact of natural disasters are particularly welcome.The present special issue seeks to the frontier of multi-hazard safety and resilience science by integrating reliability engineering, dynamic risk theory, and resilience analytics with cutting-edge methodologies from AI, multi-scale modeling, data-driven techniques, and optimization algorithms.The scope of this special issue includes, but is not limited to, the following topics:Comprehensive framework integrating disaster chain uncertainties, environmental parameters and human factors.Deep learning and reinforcement learning for cross-scale failure and system reliability prediction under insufficient data.Advanced data-driven based method for system performance with the dynamic evolution of multi-hazards.Multi-fidelity simulation and network modeling of system resilience considering interdependent sub-systems.Reliability and resilience quantification for complex engineering systems under cascading disruptive events.AI-augmented resilience optimization for complex engineering systems.Guest editors:Dr. Tao ZengAffiliation: China Academy of Safety Science and Technology, Beijing, ChinaProf. Valerio CozzaniAffiliation: University of Bologna, Bologna, ItalyProf. Genserik ReniersAffiliation: Delft University of Technology, Delft, NetherlandsProf. Lijun WeiAffiliation: China Academy of Safety Science and Technology, Beijing, ChinaManuscript submission information:Open for Submission: from 28-Feb-2026 to 28-Feb-2027Submission Site: Editorial Manager®Article Type Name: "VSI: RESS_Multi-hazard Resilience" - please select this item when you submit manuscripts onlineAll manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue.For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Dr. Tao Zeng).Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Reliability Engineering & System Safety - ISSN 0951-8320 | ScienceDirect.com by ElsevierFor more information about our Journal, please visit our ScienceDirect Page: Reliability Engineering & System Safety | Journal | ScienceDirect.com by ElsevierKeywords:Resilience assessment, Resilience enhancement, Complex engineering system, Multi-hazard, Multi-scale modeling, AI-driven method, Natech
2026-04-20
PUBLICATIONS
- Sustainable energies and machine learning: an organized review of recent applications and challenges
- Pouya Ifaei, Morteza Nazari-Heris, Amir Saman Tayerani Charmchi, Somayeh Asadi, ChangKyoo Yoo*, Sustainable energies and machine learning: an organized review of recent applications and challenges, Energy (ISSN: 0360-5442, SCI, Top 5% journal – THERMODYNAMICS), 266(1), pp.126432 (2023.3) (Ack 3개: Brain Pool Program2019H1D3A1A02071051 & NRF중견2021R1A2C2007838 & Collabo R&D of SMEs and Startups in 2022.(Project No. S3301144)
- Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach
- Shahzeb Tariq+, Jorge Loy-Benitez+, KiJeon Nam, SangYoun Kim, MinJeong Kim*, ChangKyoo Yoo*, Deep-AI soft sensor for sustainable health risk monitoring and control of fine particulate matter at sensor devoid underground spaces: A zero-shot transfer learning approach, Tunnelling and underground space (ISSN: 0886-7798, SCIE, JCR Top 10%-ENGINEERING, CIVIL, DOI), Elsevier, 131(1), pp.104843 (Ack: 2021R1A2C2007838 & Fine Dust Reduction Technology(21QPPW-B152306-03)
- Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving
- Abdulrahman H. Ba-Alawi, KiJeon Nam, SungKu Heo, TaeYong Woo, Hanaa Aamer, and Chang Kyoo Yoo*, Explainable multisensor fusion-based automatic reconciliation and imputation of faulty and missing data in membrane bioreactor plants for fouling alleviation and energy saving, Chemical Engineering Journal (IF>16.4, JCR TOP 3% journal/Top 1 journal-Chemical Engineering), 452(1), pp.139220 (2023.1) (Ack 3개:NRF2021R1A2C2007838, “Prospective green technology innovation project (2020003160009), SMEs and Startups in 2022 (Project No. S3301144)
