Machine Health Diagnosis & Prognosis by Advanced Sensing and Data Driven Techniques
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 5966
Special Issue Editors
Interests: predictive maintenance; digital twin; signal processing; machine learning; system reliability analysis; remaining useful life prediction; time–frequency analysis
Special Issues, Collections and Topics in MDPI journals
Interests: condition monitoring; fault diagnosis; fault prognosis; vibration analysis; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: reliability assessment; process improvement; bayesian inference; machine learning; accelerated test design; failure analysis; industrial safety; stochastic degradation modelling
Interests: information fusion; digital twin technology; structural health monitoring; fault diagnosis and prognosis; system reliability analysis; dynamic modeling of mechanical systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are pleased to invite you to submit your papers to the Special Issue of Machines on “Machine Health Diagnosis and Prognosis by Advanced Sensing and Data Driven Techniques”. Over the last few decades, researchers have paid close attention to machine diagnosis and prognosis. A multifaceted strategy in terms of diagnostic and prognostic research themes can greatly increase the operational dependability of machine maintenance. In terms of fundamental and engineering applications, modern technologies such as as smart sensing, sophisticated data collection, and intelligent algorithms have made this subject more appealing to researchers. Machine health diagnosis and prognosis are widely used in industries such as aerospace, manufacturing, automotive, infrastructure, and transportation sectors. Given the present state-of-the-art methods in this fast-evolving research field, we are soliciting papers in all areas of machine-health diagnostic and prognostic operations. A comprehensive range of issues in the field of machine diagnostics and prognostics will be discussed, including novel theories, techniques, data gathering processes, optimization algorithms, sensor design, modelling, and so on.
Topics include but are not limited to the following:
(1) Development of dynamic modelling regarding fault inception mechanisms and the degradation of the machine health;
(2) Signal processing relative to measured data;
(3) Remaining useful life prediction;
(4) Artificial intelligence-based algorithms for machine-health diagnosis and prognosis;
(5) Hybrid fault diagnosis techniques;
(6) Machine-health monitoring under non-stationary time-varying speed conditions;
(7) Structural health monitoring (SHM);
(8) Feature extraction from machine data;
(9) Non-destructive testing (NDT);
(10) Machine learning;
(11) Deep learning.
Dr. Khandaker Noman
Dr. Anil Kumar
Dr. Shah Limon
Dr. Yongbo Li
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- condition monitoring (CM)
- prognostic and health management (PHM)
- structural health monitoring (SHM)
- reliability analysis
- non-destructive evaluation
- data-driven techniques
- sensing technology
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