Special Issue "Machinery Diagnostics and Prognostics"

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A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: 31 October 2014

Special Issue Editors

Guest Editor
Prof. Dr. Ratna Babu Chinnam
Industrial & Systems Engineering Department, College of Engineering, Wayne State University, 5050 Anthony Wayne Dr. Detroit, MI 48202, USA
Website: http://engineering.wayne.edu/profile/ratna.chinnam/
E-Mail: Ratna.Chinnam@wayne.edu
Phone: +1-313-577-4846
Fax: +1-313-578-5902
Interests: autonomous diagnostics; prognostics; smart engineering systems; supply chain management; sustainability

Guest Editor
Prof. Dr. Andrew Ball
Department of Computing and Engineering, The University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK
Website: http://www.hud.ac.uk/ourstaff/profile/index.php?staffuid=vcapab
E-Mail: a.ball@hud.ac.uk
Interests: detection and diagnosis of faults in mechanical; electrical and electro-hydraulic machines; data analysis and signal processing

Special Issue Information

Dear Colleagues,

Considerable advances in sensing hardware, information technologies, reasoning systems, and software algorithms, are leading to significant new developments in the areas of equipment health monitoring, fault diagnosis, and prognosis. These advances are enabling industries to undergo a fundamental shift towards condition based maintenance to improve equipment availability and readiness at reduced operating cost throughout the system life-cycle. Emergence of sensor networks is also bringing the possibility of collective learning algorithms and decision-theoretic approaches to facilitate effective and scalable diagnostics/prognostics technology for widespread deployment of condition-based maintenance. This special issue aims to bring together papers that report recent advances and challenges in designing and developing state-of-the-art fault diagnosis and prognosis systems and algorithms. Of particular interest are original contribution papers that demonstrate successful application of the algorithms and methods to complex equipment in a variety of industries. We hope that this special issue will be useful and informative to both researchers and practitioners. We also hope to deliver readers promising new ideas and directions for future research.

Suitable topics for this special issue include but are not limited to:

• Data-driven methods for anomaly detection, diagnosis, and prognosis
• Model-based methods for fault detection, diagnosis, and prognosis
• Health management system design and engineering
• Physics of failure modeling and simulation
• Health monitoring sensors and sensing
• Industrial applications

Prof. Dr. Ratna Babu Chinnam
Prof. Dr. Andrew Ball
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Published Papers (1 paper)

Machines 2014, 2(1), 87-98; doi:10.3390/machines2010087
Received: 24 December 2013; in revised form: 10 February 2014 / Accepted: 17 February 2014 / Published: 25 February 2014
Show/Hide Abstract | Download PDF Full-text (256 KB) | View HTML Full-text | Download XML Full-text

Last update: 3 December 2013

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