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)

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p. 87-98
by , , ,  and
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
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(This article belongs to the Special Issue Machinery Diagnostics and Prognostics)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Type of Paper: Article
Title: Residual Generator Fuzzy Identification For Wind Turbine Benchmark Fault Diagnosis
Author: Silvio Simani
Affiliation: Università degli studi di Ferrara, DIPARTIMENTO DI INGEGNERIA c/o DIPARTIMENTO DI INGEGNERIA Via Saragat 1 44121 – Ferrara, Italy
Abstract:
In order to improve the safety of wind turbines and to avoid catastrophic consequences, it is important to detect and isolate faults in their earlier occurrence. The main problem of model--based fault diagnosis applied to wind turbines is represented by the system complexity, as well as the uncertainty of the available measurements. In this work, a data--driven strategy relying on fuzzy  models is presented, in order to build a fault diagnosis system. Fuzzy theory is exploited here since it allows to approximate unknown models and manage uncertain data. Moreover, the use of fuzzy models, which are directly identified from the wind turbine measurements, allows the design of the fault detection and isolation module. It is worth noting that, in general, the nonlinearity of wind turbine system would lead to quite complex analytic solutions. However, IF--THEN fuzzy rules lead to a simpler solution, important when on--line implementations have to be considered. The wind turbine benchmark is used to validate the achieved performances of the suggested fault detection and isolation scheme. Finally, comparisons of the proposed methodology with respect to different fault diagnosis methods serve to highlight the features of the suggested solution.

Last update: 21 May 2014

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