Dear Colleagues,

I would like to invite you to submit your work to a Special Issue on “New Trends in Machine Diagnostic and Condition Monitoring”. In recent years, we have seen renewed interest in the development of algorithms and techniques for machine diagnostics. Novel industrial paradigms of flexibility, energy management and sustainability of processes and products have led companies to adopt new technologies with better performance, but also challenging from the condition monitoring point of view. For example, the introduction of servomotors in non-stationary motion as electric cams in the industry, which required the adoption of new techniques such as cyclostationarity and/or cyclic non-stationarity.

Today, further technological steps are ongoing in different mechanical fields. A fleet of independent linear motors moving on the same rail(s) is the new frontier of automation engineering recently proposed by most automation manufacturers. This increased flexibility is counterbalanced by the difficulty of diagnosing damage to bearings, which become much smaller and more numerous, subject to highly non-stationary working conditions and with a very low signal-to-noise ratio. This is just an example of many application fields where the state-of-the-art of vibration-based condition monitoring needs to be improved and tailored to overcome the technological challenges of the coming years.

Thus far, technological progress has also brought new tools for machine maintenance. The measurement of vibrations is often accompanied by the acquisition of other parameters, such as the currents absorbed by the electric motors or the instantaneous speed of a shaft. With regards to the data analysis, we cannot fail to mention the increasingly intensive use of machine learning and deep learning techniques. However, data science cannot replace a clear idea of the physical phenomena underlying the damage mechanisms of mechanical components.

In this Special Issue, I would like to commence a dialogue with experts of machine diagnostics and condition monitoring to better understand what the current challenges are, those expected in the near future, and how to address them. Original papers concerning novel approaches, techniques and instrumentations to model and diagnose the occurrence of damage in machines are welcome, as well as contributions regarding possible challenging fields of application, highlighting critical issues and suggesting possible approaches.

Contributions can take the form of either research papers or comprehensive review articles.

Dr. Marco Cocconcelli
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

• fault detection, diagnostics and prognostics of machinery
• methodologies for the quantitative characterization of faults
• advance signal processing techniques for condition monitoring
• sensor-fusion techniques for damage detection
• experimental test cases on novel-concept machinery
• new and emerging measurement and analysis technologies
• new developments in modeling and simulations of faults in machinery