Background

Prognostics and health management (PHM) of engineering systems and critical components has an important meaning in the system-health management of industrial equipment. Based on sensor systems, health condition recognition takes a measure to respond to the anticipation of failures and minimizes economic loss, and then prevents any unexpected accidents. Thanks to the development of computer science, a large amount of direct and indirect health monitoring data is researched for fault diagnosis. Those fault diagnosis techniques can generally be divided into two categories: model-based and data-driven ones. To reveal the relation between fault mechanism and parameters of the mechanical system, model-based diagnosis usually establishes dynamic models of the machinery by means of dynamics, finite elements, and modal analyses. In recent years, several researchers have made a lot of contributions based on data-driven methods, such as shallow learning, deep learning, or deep transfer learning for machine fault diagnosis. Once a health indicator is available, prognostic algorithms are developed to extrapolate the current health condition to future health conditions and then predict the remaining useful life.

This special section of the International Journal of Performability Engineering (IJPE) aims to provide an open, multidisciplinary forum for recent advances in machinery fault diagnostics, such as signal processing, deep learning, transfer learning, wavelet network, and data fusion.

Submission

We are soliciting original contributions that have not been published and are not currently under consideration elsewhere. Both theoretical studies and state-of-the-art practical applications are welcome. All submitted papers will be peer-reviewed and selected on the basis of their quality and relevance to the theme of this special section.

We also encourage extensions of conference papers, unless prohibited by copyright, if there is a significant difference in the technical content. Improvements such as adding a new case study or including a description of additional related studies do not satisfy this requirement. A description explaining the difference between the conference paper and the journal submission is required. The overlap between each submission and other articles, including the authors’ own papers and dissertations, should be less than 30%. Each submission must conform to the IJPE template. Please click here to submit your paper.

Special Attention

Important Dates

Guest Editors