Dear Colleagues,

In modern industries, the monitoring of machinery health is essential for the adoption of predictive maintenance strategies. Indeed, the constant control of the machinery condition in real time enables the early detection of potential faults and the prevention of unnecessary maintenance operations, while preserving the performance of the system.

In condition-based maintenance, machinery health diagnosis and prognosis play a prominent role, and complement each other. The former is devoted to analyzing the historical and current health status of the system based on the signals being monitored; the latter is devoted to predicting the remaining functional life of the machinery based on its past and current operation profiles.

For the diagnosis and prognosis of machinery health, different approaches have been proposed. Among these, data-driven frameworks that resort to machine learning and deep learning methods have recently attracted great attention, and are regarded as a powerful and more flexible alternative compared to physics-based models which, for example, cannot be updated with ongoing health data measurements. Despite the fact that data-driven approaches are promising in terms of identifying the relationship between machinery data and health status, capturing the early symptoms of machinery breakdown and predicting the future working capabilities of the machine, based on the monitored signals, are relatively difficult; this still represents a challenge in the field of machinery health management.

This Special Issue is addressed to both researchers and industrial professionals, and aims to collect the latest theoretical and applied advancements in the field of data-driven approaches to the health monitoring of intelligent machinery. The submission of the results of experimental research is encouraged. The theoretical papers accepted into this Special Issue are expected to present novel contributions and potentially viable solutions to real industrial problems of practical interest.

Prof. Carlotta Orsenigo
Dr. Masoud Jalayer
Guest Editors

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 2400 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.

• predictive maintenance
• machine diagnostics
• machine prognostics
• machine learning
• deep learning