Special Issue "Advances in Machinery Condition Monitoring, Diagnosis and Prognosis"
A special issue of Machines (ISSN 2075-1702).
Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 960
Interests: structural health monitoring; machinery fault diagnosis and prognosis; smart structures, materials and sensing; advanced manufacturing
Interests: reliability-based analysis and design; failure prognostics and diagnostics; uncertainty quantification
Special Issues, Collections and Topics in MDPI journals
Machinery plays a vital role in many industries. The main machinery components include the shaft, bearing, gears, blades, motors and engines, and so on. Since these components are subject to harsh operation conditions, they are prone to failure, which causes the machinery breakdown and, consequently, tremendous economic loss. Therefore, conducting timely and accurate prognosis and diagnosis of machinery is critically important to maintaining the machinery integrity and operational reliability.
This Special Issue of Machines solicitates the novel research studies that can make significant contributions to advancing state-of-the-art machinery monitoring, diagnosis and prognosis techniques, leading to enhanced diagnosis and prognosis accuracy, robustness and reliability in the practical applications. The research topics of interest include, but are not limited to:
- Enhanced signal processing and feature extraction for retrieving the pivot fault-related signals.
- Enhanced multi-physics system-level modeling and simulation of machinery to elucidate the consequence of component fault occurrence.
- Novel machine-learning-enabled intelligent fault diagnosis and prognosis with enhanced performance even under the limited quality and size of available dataset.
- Novel integrated/unified numerical framework/platform that leverages the synergic advancement of physical modeling, signal processing, machine learning and optimization methods.
- The demonstrations of practical and challenging fault diagnosis and prognosis applications.
- Reliability-based analysis and design of machinery considering the multifarious uncertainties.
Dr. Kai Zhou
Dr. Zequn Wang
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.
- machinery fault diagnosis and prognosis
- signal processing and feature extraction
- machine learning
- deep learning
- system-level machinery modeling
- numerical optimization
- reliability-based analysis and design