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Mechanical Systems and Signal Processing

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Factor de Impacto:
8.9
Editor:
Elsevier
ISSN:
0888-3270
Vistas:
32617
Seguidores:
6

Solicitud de Artículos

Mechanical Systems and Signal Processing is an academic journal published by Elsevier. (ISSN 0888-3270, impact factor 8.9).

Aims & Scope Mechanical Systems and Signal Processing (MSSP) is an interdisciplinary journal in Mechanical, Aerospace, and Civil Engineering aiming at reporting scientific advancements of the highest quality arising from novel concepts and techniques in signal and information processing and related topics in engineering dynamics and dynamical systems. MSSP papers are expected to make a demonstrable original contribution to engineering knowledge, which should be significant in terms of advancement over established methods. Especially sought are papers that include both theoretical and experimental aspects, or that include theoretical material of high relevance to practical applications. Scope of MSSP The scope of MSSP encompasses original research work on signal and information processing and related topics within engineering dynamics and dynamical systems, as required in Mechanical, Aerospace, and Civil Engineering. The work should focus on one (or more) of the subject areas listed below. Prospective authors must select a single best fitting subject area during manuscript submission. [A] Signal processing in machine/system health monitoring [B] Non-stationary and stationary random vibration [C] Time series methods [D] Rotor dynamics [E] Signal processing in manufacturing/machining [F] Powertrains and drivetrains [G] Acoustics, waves, and SEA [H] Control of vibration and noise [I] Structural Health Monitoring (SHM) [J] Structural/system or modal identification (including parameter estimation [K] Nonlinear vibration (including energy harvesting) [L] Uncertainty quantification [M] Prognostics [N] Smart structures and systems The following subject areas are currently outside the MSSP scope: Multi-body dynamics and robotics, including control of robots Control of vehicles Theoretical control - papers better suited to a specialist controls journal Theoretical nonlinear dynamics without experimental validation Uncertainty quantification with no clearly defined relevance to engineering dynamics
Última actualización por Dou Sun el

Special Issues

Special Issue on Generative AI for V&V in Mechanical and Structural Systems Día de Entrega: 2026-07-31 Human beings are now in the AI era. Large language models are reshaping how people work, live, and think. At the heart of these systems lie the generative neural networks, which stand apart from conventional AI architectures by learning invertible mappings between physical data and latent representations. This capability enables them to synthesize realistic new samples, perform one-shot posterior inference, and offer probabilistic reasoning. This Special Issue explores how these capabilities can be responsibly integrated into Verification and Validation (V&V) processes for mechanical systems by advancing model updating, health monitoring, uncertainty quantification, and digital twin development, while ensuring trust, transparency, and reliability in engineering practice. Guest editors: Dr. Sifeng Bi Assistant Prof in Aerospace Engineering University of Southampton, Southampton, UK (Uncertainty Quantification, Model Updating, Bayesian Approach, Data-driven Modelling, Aircraft Design) Dr. Tanmoy Mukhopadhyay Lecturer B in Materials and Structures University of Southampton, Southampton, UK (Mechanical metamaterials, Advanced multi-functional composites, Uncertainty quantification and reliability analysis, Artificial intelligence, Digital twins) Dr. Susmita Naskar Lecturer B in Mechanics of Materials and Structures University of Southampton, Southampton, UK (Mechanics of metamaterials, Deployable and reconfigurable structures, Uncertainty Quantification, Scientific machine learning algorithm, Advanced multi-functional composites, Smart materials and structures, 2D Materials and nano-heterostructures) Prof. Michael Beer Professor in Risk and Reliability Leibniz University Hannover, Hannover, Germany (Structural and Systems Reliability, Uncertainty Quantification, Imprecise Probabilities) Prof. John Mottershead Professor in Mechanical and Aerospace Engineering University of Liverpool, Liverpool, UK (Vibrations, Model Updating, Uncertainty Quantification) Special issue information: Model Verification & Validation (V&V) is essential for ensuring the safety, reliability, and performance of mechanical systems. Key V&V techniques include code verification, solution verification, and experimental validation. Uncertainty quantification and sensitivity analysis support these processes by characterising and reducing uncertainty, thereby enhancing the trustworthiness and robustness of V&V. Generative AI is now reshaping how these V&V tasks are carried out. On the one hand, deep generative models enable adaptive surrogate solvers, automated design exploration and live health diagnostics, facilitating direct, real-time inference for tasks such as structural optimisation, reliability assessment and condition monitoring. On the other hand, the data driven nature of these methods can obscure core physical principles and reduce interpretability. This special issue thus brings together experts to not only advance generative AI applications but also examine its underlying principles, novel customizations, potential risks, and frameworks for responsible deployment in mechanical, structure, and aerospace applications. Topics of the SI include the integration of generative AI with:• Model Updating & Calibration Structural Health Monitoring & Damage Detection Uncertainty Quantification & Propagation Reliability & Risk Analysis Surrogate Model Construction Digital Twins & Real-Time Decision Support Physics-Informed and Explainable Neural Networks Responsible and Trustworthy Use of Generative AI Manuscript submission information: Submission Information Manuscript submission open date: 8 August 2025 Manuscript submission deadline: 31 July 2026 You 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. Sifeng Bi via sifeng.bi@soton.ac.uk. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of "VSI: Generative AI in V&V" 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: Generative AI, Verification and Validation, Model Updating, Uncertainty Quantification, Digital Twins, Structure Health Monitoring, Mechanical Systems
Última actualización por Dou Sun el
Special Issue on Uncertainty-aware Structural Health Monitoring Día de Entrega: 2026-09-30 Structural health monitoring (SHM) plays a vital role in ensuring the safety and reliability of structures by continuously tracking their operational status and detecting potential damage. However, uncertainty is inherent in the entire SHM process, arising from sensor measurements, environmental conditions, model simplifications, as well as manufacturing/assembling processes, etc. These uncertainties can significantly affect the accuracy of structural condition assessment, damage diagnosis, and reliability predictions, thereby posing challenges to informed decision-making in structural maintenance and management. This Special Issue aims to gather high-quality original research papers and review articles that advance resilient SHM systems. Submissions should place uncertainty at the core of SHM, treating it explicitly and quantitatively. We particularly welcome advances in methods and frameworks for uncertainty quantification (UQ), propagation, and decision-making under uncertainty. We also seek contributions that explore innovative theories and methodologies, addressing practical applications in this field, with the goal of promoting the development of reliable and efficient SHM practices. Guest editors: Associate Professor Xinyu Jia Ph.D. Hebei University of Technology, Tianjin, China (Structural health monitoring; Bayesian inference; Uncertainty quantification; Reliability analysis) Professor Eleni Chatzi Ph.D. ETH Zurich, Zurich, Switzerland (Structural health monitoring; Machine learning; Uncertainty quantification; Nonlinear dynamics) Professor Costas Papadimitriou Ph.D. University of Thessaly, Volos, Greece (Uncertainty quantification; Bayesian inference; Structural dynamics; Structural health monitoring) Special issue information: Topics of interest within the SHM context include: UQ and propagation in structural dynamics Reliability updating in performance predictions Machine learning for uncertainty-aware SHM Model uncertainty in SHM of complex structures Physics-informed neural networks (PINNs) Probabilistic/non-probabilistic methods for damage identification Model updating and sensitivity analysis under uncertainty Batch or sequential Bayesian techniques for input-state-parameter estimation Reliability-based design and life-cycle performance assessment Integration of UQ in digital twin frameworks Robust optimal sensor placement strategies Uncertainty-based decision making Manuscript submission information: Manuscript submission open date: 10 December 2025 Manuscript submission deadline: 30 September 2026 You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Professor Costas Papadimitriou. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: Uncertainty-aware SHM” 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: Structural health monitoring; Uncertainty quantification; Reliability updating; Model updating; Digital twin
Última actualización por Dou Sun el
Special Issue on Progress in the Inverse Finite Element Method (iFEM) for Deformation Reconstruction and Structural Health Monitoring Día de Entrega: 2026-11-01 In the evolving field of Structural Health Monitoring (SHM), the ability to measure, reconstruct, and predict the behavior of engineering structures under operational conditions has become indispensable. The increasing complexity of modern aerospace, marine, civil, and mechanical systems require advanced monitoring strategies that can ensure safety, durability and performance optimization throughout the life cycle of structures. This demand has driven rapid progress in both sensor technologies and inverse computational frameworks, enabling new paradigms in real-time monitoring. Among these advances, the Inverse Finite Element Method (iFEM) has emerged as a particularly powerful and timely solution. Originally developed as a general-purpose inverse problem-solving framework, iFEM reconstructs full-field displacements, strains, and stresses from sparse strain measurements. Its foundation in finite element theory makes it inherently versatile, allowing application to diverse materials, geometries and loading scenarios. As structural systems become more lightweight, adaptive and integrated with multi-functional components, iFEM provides a robust, scalable and computationally efficient means of deformation reconstruction, bridging the gap between measured data and actionable insights. The timeliness of iFEM is underscored by its unique ability to support structural digital twins, dynamic and high-fidelity virtual representations that continuously assimilate sensor data to mirror the state of physical assets. Through iFEM, sparse in-situ measurements are transformed into comprehensive full-field structural responses, enabling digital twins to provide predictive capabilities for damage progression, fatigue and failure modes. This integration forms the cornerstone of next-generation SHM systems, where real-time monitoring is not only descriptive but also prescriptive, offering decision support for proactive maintenance and operational efficiency. Furthermore, iFEM aligns naturally with advances in signal processing and data-driven modeling. Modern SHM relies heavily on filtering, statistical analysis and spectral methods to interpret sensor outputs. By coupling iFEM with these signal processing techniques, it becomes possible to enhance robustness against noise, quantify uncertainties and improve the reliability of structural diagnostics. Hybrid physics–data-driven frameworks that combine iFEM formulations with machine learning or reduced-order models are also emerging as promising strategies, delivering both interpretability and computational efficiency. Applications of iFEM have already been demonstrated in a wide range of engineering domains. In aerospace engineering, it enables in-flight deformation reconstruction of wings, fuselages and morphing structures. In marine and offshore systems, it supports monitoring of hull integrity and wave-induced responses. In civil infrastructure, iFEM provides a pathway to assess bridges, pipelines and towers under operational loads. In mechanical engineering, it is applied to rotating machinery, composite laminates and lightweight structures where conventional sensing methods fall short. Across these domains, iFEM demonstrates a consistent capability to deliver accurate, real-time deformation and damage information using only minimal sensor networks—an essential feature for cost-effective monitoring. Guest editors: Associate Professor Adnan Kefal Ph.D., University of Strathclyde (UK) Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Türkiye (Structural Health Monitoring; Fiber Optic Sensing; Finite Element Methods; Computational Mechanics; Composite Structures) Associate Professor Claudio Sbarufatti Ph.D., Politecnico di Milano (Italy) Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy (Structural Health Monitoring; Digital-Twin; Prognostics and Health Management; Machine Learning for Structural Systems; Composite Materials) Dr. Jacopo Bardiani Ph.D., Politecnico di Milano (Italy) Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy (Structural Health Monitoring; Inverse Finite Element Method (iFEM); Damage Detection and Localization; Composite and Smart Structures; Experimental Mechanics) Special issue information: The aim of this Special Issue is to present cutting-edge research and applications of iFEM and related inverse problem methodologies in SHM, Digital Twin development and signal processing. Submissions are encouraged that address the following broad themes: Innovative theoretical, computational, and experimental developments of iFEM for shape, strain, and stress sensing. Integration of iFEM with signal processing, machine learning, and hybrid physics–data-driven approaches for robust deformation reconstruction. Novel iFEM algorithms for damage diagnosis (detection, identification/ characterization, severity estimation) and prognosis, leveraging reconstructed fields of displacement, strain, and stress. Sensor optimization strategies including placement, network design and adaptive sensing for iFEM analyses. Nonlinear and dynamic formulations of iFEM, addressing vibration monitoring, fatigue assessment and fracture detection. Applications in Digital Twin ecosystems, showcasing how iFEM enhances predictive maintenance and decision-making frameworks. Cross-disciplinary applications of iFEM across aerospace, marine, civil, and mechanical systems. By consolidating state-of-the-art contributions, this Special Issue seeks to advance the frontier of SHM and structural digital twins, bringing together the computational mechanics, intelligent sensing and signal processing communities. The outcome will be a comprehensive reference that not only surveys the present achievements of iFEM but also outlines the future challenges and opportunities for its adoption in critical engineering applications. Manuscript submission information: Manuscript submission open date: 1 April 2026 Manuscript submission deadline: 1 November 2026 You are invited to submit your manuscript at any time before the submission deadline. For any inquiries about the appropriateness of contribution topics, please contact Associate Professor Adnan Kefal. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: iFEM for SHM” 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: structural health monitoring; inverse problem; deformation reconstruction; inverse finite element method (iFEM); damage diagnosis and prognosis; sensor optimization; structural systems; signal processing; digital twin
Última actualización por Dou Sun el

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