Data-Driven Approaches in Acoustics and Vibration for Predictive Diagnostics

Dear Colleagues,

The reliable prediction of failures in mechanical systems increasingly relies on data-driven analysis of acoustic and vibration responses. This Special Issue, “Data-Driven Approaches in Acoustics and Vibration for Predictive Diagnostics”, aims to gather high-quality original research and review articles that explore how modern signal processing, machine learning, and artificial intelligence can be leveraged for condition monitoring, fault detection, and prognostics in machines and structures.

We welcome contributions that address the full pipeline from raw acoustic/vibration data acquisition to feature extraction, modeling, classification, and Remaining Useful Life (RUL) estimation. Of particular interest are works that combine physics-based insight with data-driven models, propose interpretable or trustworthy AI methods, or demonstrate robust performance under realistic operating conditions (noise, variable loads, environmental uncertainty).

Topics of interest include, but are not limited to, the following:

• Data-driven condition monitoring using acoustic and vibration signals;
• Machine learning and deep learning for fault detection and diagnosis;
• Time–frequency, time–scale, and nonlinear signal processing (e.g., wavelets, HHT, VMD);
• Structural health monitoring and non-destructive evaluation based on acoustics and vibration;
• Acoustic emission and ultrasonic techniques for early damage detection;
• Physics-informed and hybrid modeling approaches for predictive diagnostics;
• Prognostics and health management (PHM) and RUL estimation;
• Real-time implementation, edge/embedded solutions, and industrial case studies.

This Special Issue seeks contributions from academia and industry that advance the state of the art and demonstrate the practical impact of data-driven acoustics and vibration for predictive maintenance and smart machines.

Dr. David Isaac Ibarra-Zarate
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 250 words) can be sent to the Editorial Office for assessment.

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 diagnostics
• condition monitoring
• data-driven methods
• machine learning
• deep learning
• acoustics and vibration
• structural health monitoring
• fault detection and diagnosis
• non-destructive evaluation
• prognostics and health management