《機(jī)械系統(tǒng)與信號(hào)處理》（MSSP）是機(jī)械、航天和土木工程領(lǐng)域的一本跨學(xué)科期刊，旨在報(bào)告?zhèn)鞲?、儀表、信號(hào)處理、建模和控制動(dòng)態(tài)系統(tǒng)等新技術(shù)所帶來的最高質(zhì)量的科學(xué)進(jìn)步。MSSP論文有望對(duì)工程知識(shí)做出可證明的原始貢獻(xiàn)，這在現(xiàn)有方法的進(jìn)步方面應(yīng)具有重要意義。特別尋求的是包括理論和實(shí)驗(yàn)方面的論文，或者包括與實(shí)際應(yīng)用高度相關(guān)的理論材料。MSSP是該領(lǐng)域的領(lǐng)導(dǎo)者，其研究領(lǐng)域包括：1、啟動(dòng)、感應(yīng)和控制?振動(dòng)和噪音控制?行波?智能材料系統(tǒng)?壓電?適應(yīng)能力?集成系統(tǒng)2、測(cè)量與信號(hào)處理?理解機(jī)械系統(tǒng)的信號(hào)處理?全場(chǎng)振動(dòng)/聲學(xué)測(cè)量?大數(shù)據(jù)問題3、非線性?非線性振動(dòng)問題?非線性正常模式?能量收集4、旋轉(zhuǎn)機(jī)械、機(jī)械診斷和SHM?診斷和預(yù)測(cè)?轉(zhuǎn)子動(dòng)力學(xué)?轉(zhuǎn)子裂紋?軸承和齒輪5、不確定性的量化?概率、間隔和模糊分析?可靠性和魯棒性?貝葉斯方法6、振動(dòng)、模態(tài)分析和結(jié)構(gòu)?結(jié)構(gòu)建模與識(shí)別?反問題?運(yùn)行模式分析?環(huán)境振動(dòng)測(cè)試提交給MSSP的文件應(yīng)在附信中明確說明工作的原始科學(xué)貢獻(xiàn)。這也應(yīng)在摘要中簡(jiǎn)要說明，并在引言中加以擴(kuò)展。此外，在導(dǎo)言中，重要的是要清楚地定義用所有條件和假設(shè)來處理的具體問題，并將貢獻(xiàn)與歷史文獻(xiàn)（通常按時(shí)間順序排列）和藝術(shù)現(xiàn)狀聯(lián)系起來。應(yīng)盡可能對(duì)最新技術(shù)進(jìn)行總結(jié)和分類，但不應(yīng)僅列出文件。引入新方法或方法的具體原因應(yīng)在現(xiàn)有技術(shù)的基礎(chǔ)上變得明確。應(yīng)清楚詳細(xì)地說明所提出方法相對(duì)于已建立技術(shù)的任何優(yōu)勢(shì)，包括盡可能進(jìn)行的比較試驗(yàn)和實(shí)驗(yàn)證據(jù)。MSSP旨在保持高標(biāo)準(zhǔn)的書面英語，作者有責(zé)任確保該語言是可理解的。不這樣做可能會(huì)導(dǎo)致你的論文被拒。具有機(jī)器學(xué)習(xí)或信號(hào)處理內(nèi)容的論文作者應(yīng)參閱有關(guān)這些主題的MSSP指南：http://media.journals.elsevier.com/content/files/machine-learning-04180327.pdfhttps://www.elsevier.com//uuuu data/promis_misc/signalprocessing.pdf

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