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Special Issue "Fault Diagnosis and Health Management of Power Machinery"
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 22890
Special Issue Editors
Interests: machinery condition monitoring; intelligent fault diagnosis and prognostics; deep learning
Interests: machine learning; interpretable AI; fault diagnosis; condition monitoring
Interests: sparse representation; machine learning; deep learning; condition monitoring
Interests: diagnosis; prognosis; machine learning; reliability and asset management; condition monitoring
Special Issue Information
Modern power-machinery systems are typically operated in harsh operating and environmental conditions. Unexpected failures of such systems have been frequently reported and have severe consequences for production, businesses, and society, which leads to higher operating and maintenance costs. Therefore, it is of significance to develop a proactive program by which to effectively reduce unexpected failures and further improve the effectiveness of power-machinery operation. The advances in real-time-sensor monitoring techniques bring tremendous opportunities to enhance the reliability and safety of power-machinery systems. Particularly, the diagnosis process assists in the identification/classification of machinery faults in terms of severity and type. The knowledge from diagnosis is also utilized to quantify the machinery’s health state and track the evolution of machinery performance degradation in support of its remaining useful life (RUL) prognosis.
This Special Issue aims to collect original ideas for the fault diagnosis and prognosis of power-machinery systems. The guest editors invite original contributions on the following topics, but authors are not limited to these:
- Constructing health indicators;
- Sensor-data fusion techniques;
- Condition monitoring and intelligent fault diagnosis;
- Data-driven, physics-based and hybrid prognostic strategies;
- Machine learning in fault diagnosis and prognosis;
- The integration of diagnostic and prognostic decisions in maintenance strategies;
- Uncertainty quantification.
Dr. Te Han
Dr. Ruonan Liu
Dr. Zhibin Zhao
Dr. Pradeep Kundu
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 2000 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.
- condition monitoring
- condition-based maintenance
- machine learning
- power machinery