Mechanical Systems and Signal Processing

Mechanical Systems and Signal Processing - ISSN 0888-3270
Source Normalized Impact per Paper (SNIP): 3.347 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 2.191 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 6.471 (2019) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
5 Year Impact Factor: 6.308 (2019) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2016 to the previous five years and divided by the source items published in the previous five years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
Volumes: Volume 16
Issues: 16 issues
ISSN: 08883270
Editor-in-Chief: Mottershead

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Description

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:

  • Signal processing in machine health monitoring
  • Non-stationary and random vibrations
  • Time series methods
  • Rotor dynamics
  • Signal processing in manufacturing/machining
  • Powertrains and drivetrains
  • Acoustics, waves and SEA
  • Control of vibrations and noise
  • Structural health monitoring
  • Structural identification
  • Nonlinear vibrations (including energy harvesting)
  • Uncertainty quantification in engineering dynamics



The following research areas are considered to be outside the scope of MSSP:

  • 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



Papers 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: https://media.journals.elsevier.com/content/files/machine-learning-04180327.pdf
https://www.elsevier.com/__data/promis_misc/SignalProcessing.pdf