PHM 2021 is collaborating with IEEE Transactions on Reliability. IEEE Transactions on Reliability is a refereed journal for the reliability and allied disciplines including, but not limited to, maintainability, physics of failure, life testing, prognostics, design and manufacture for reliability, reliability for systems of systems, network availability, mission success, warranty, safety, and various measures of effectiveness. Topics eligible for publication range from hardware to software, from materials to systems, from consumer and industrial devices to manufacturing plants, from individual items to networks, from techniques for making things better to ways of predicting and measuring behavior in the field. As an engineering subject that supports new and existing technologies, we constantly expand into new areas of the assurance sciences.
In keeping with IEEE's continued commitment to providing options to support the needs of all authors, IEEE is introducing a new Reliability Society Section in IEEE Access, IEEE's award-winning, multidisciplinary open access journal.
Be among the first to have your article peer-reviewed and published in this new Section. This is an exciting opportunity for your research to benefit from the high visibility and interest the journal's marketing launch will generate. Your work will also be exposed to 5 million unique monthly users of the IEEE Xplore® Digital Library.
The Reliability Society Section in IEEE Access draws on the expert technical community to continue IEEE's commitment to publishing the most highly-cited content. The topical editor is Professor Steven Li, of Western New England University. Our goal is to publish quickly—the Journal peer-review process targets a publication period of 6 weeks for most accepted papers.
This journal is fully open and compliant with funder mandates, including Plan S. To submit a paper, please go to the following website.
Extended papers from PHM 2021 are encouraged to be submitted to the special section on AI Enhanced Reliability Assessment and Predictive Health Management.
The special section will be focusing on the following theme: the rapid advances of Internet-of-Things (IoT) and big-data technologies, there have been increasing interests in the development and implementation of advanced artificial intelligence methods to address reliability and prognostic challenges in various industrial systems. This would potentially complement the downsides of the conventional physics-based models and statistical models that might not sufficiently account for the dynamic natures of complex engineering systems. To this end, diverse types of artificial intelligence methods have been developed in light of the massive and multi-dimensional data collected through the sensors and IoT devices. In particular, advanced AI methods, such as deep learning, transfer learning and reinforcement learning, are well-suited to utilize big-data to enhancing the reliability and prognostics of industrial systems, from the aspects of condition monitoring to predictive maintenance.
Follow the guidelines in “Information for Authors” in the IEEE Transaction on Industrial Informatics http://www.ieee-ies.org/pubs/transactions-on-industrial-informatics. Please submit your manuscript in electronic form through Manuscript Central web site: https://mc.manuscriptcentral.com/tii. On the submitting page #1 in popup menu of manuscript type, select: SS on AI Enhanced Reliability Assessment and Predictive Health Management.