MECHANICAL SYSTEMS AND SIGNAL PROCESSING

期刊基本信息

期刊名称：MECHANICAL SYSTEMS AND SIGNAL PROCESSING

出版国家或地区：ENGLAND

是否OA：No

期刊ISSN：0888-3270

期刊官方网站：http://www.journals.elsevier.com/mechanical-systems-and-signal-processing/

期刊投稿网址：http://ees.elsevier.com/ymssp/default.asp

通讯方式：ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON, ENGLAND, NW1 7DX

涉及的研究方向：工程技术-工程：机械

出版周期：Bimonthly

出版年份：1987

期刊数据表：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING英文简介：

Mechanical Systems and Signal Processing (MSSP) is an interdisciplinary journal in Mechanical, Aerospace and Civil Engineering with the purpose of reporting scientific advancements of the highest quality arising from new techniques in sensing, instrumentation, signal processing, modelling and control of dynamic 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. MSSP is a leader in its field and research areas covered include:1. Actuation, Sensing and Control• Vibration & noise control• Travelling waves• Smart-material systems• Piezoelectrics• Adaptivity• Integrated systems2. Measurement & Signal Processing• Signal processing for the understanding of mechanical systems• Full-field vibration/acoustic measurements• Big data problems3. Nonlinearity• Nonlinear vibration problems• Nonlinear normal modes• Energy harvesting4. Rotating Machines, Machinery Diagnostics & SHM• Diagnostics and prognostics• Rotor dynamics• Cracks in rotors• Bearings and gears5. Uncertainty Quantification• Probabilistic, interval & fuzzy analysis• Reliability and robustness• Bayesian methods6. Vibrations, Modal Analysis & Structures• Structural modelling & identification• Inverse problems• Operational modal analysis• Ambient vibration testingPapers submitted to MSSP should include in the covering letter a clear statement of the original scientific contribution of the work. This should also be stated briefly in the Abstract and expanded upon in the Introduction. Also in the Introduction it is important to clearly define the specific problem treated with all conditions and assumptions made, and to place the contribution in relation to both the historical literature (usually in chronological order) and the state of art. The state of the art should, as much as possible, be summarised and classified but not given as a mere listing of papers. The specific reason(s) for introducing a new method or approach should become clear based on the presented state of the art. Any advantages of proposed methods over established techniques should be explained clearly and in detail, including comparative tests and experimental evidence wherever possible.MSSP aims to maintain a high standard of written English and it is the authors' responsibility to ensure that the language is intelligible. Failure to do so may result in rejection of your paper.Authors of papers with Machine-Learning or Signal Processing content should see the MSSP guidelines on these subjects: http://media.journals.elsevier.com/content/files/machine-learning-04180327.pdfhttps://www.elsevier.com/__data/promis_misc/SignalProcessing.pdf

MECHANICAL SYSTEMS AND SIGNAL PROCESSING中文简介：

《机械系统与信号处理》（MSSP）是机械、航天和土木工程领域的一本跨学科期刊，旨在报告传感、仪表、信号处理、建模和控制动态系统等新技术所带来的最高质量的科学进步。MSSP论文有望对工程知识做出可证明的原始贡献，这在现有方法的进步方面应具有重要意义。特别寻求的是包括理论和实验方面的论文，或者包括与实际应用高度相关的理论材料。MSSP是该领域的领导者，其研究领域包括：1、启动、感应和控制•振动和噪音控制•行波•智能材料系统•压电•适应能力•集成系统2、测量与信号处理•理解机械系统的信号处理•全场振动/声学测量•大数据问题3、非线性•非线性振动问题•非线性正常模式•能量收集4、旋转机械、机械诊断和SHM•诊断和预测•转子动力学•转子裂纹•轴承和齿轮5、不确定性的量化•概率、间隔和模糊分析•可靠性和鲁棒性•贝叶斯方法6、振动、模态分析和结构•结构建模与识别•反问题•运行模式分析•环境振动测试提交给MSSP的文件应在附信中明确说明工作的原始科学贡献。这也应在摘要中简要说明，并在引言中加以扩展。此外，在导言中，重要的是要清楚地定义用所有条件和假设来处理的具体问题，并将贡献与历史文献（通常按时间顺序排列）和艺术现状联系起来。应尽可能对最新技术进行总结和分类，但不应仅列出文件。引入新方法或方法的具体原因应在现有技术的基础上变得明确。应清楚详细地说明所提出方法相对于已建立技术的任何优势，包括尽可能进行的比较试验和实验证据。MSSP旨在保持高标准的书面英语，作者有责任确保该语言是可理解的。不这样做可能会导致你的论文被拒。具有机器学习或信号处理内容的论文作者应参阅有关这些主题的MSSP指南：http://media.journals.elsevier.com/content/files/machine-learning-04180327.pdfhttps://www.elsevier.com//uuuu data/promis_misc/signalprocessing.pdf

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