Intelligent fault diagnosis in various industrial fields such as aerospace, shipbuilding, manufacturing, sustainable energy, infrastructure, and transportation has attracted increasing attention, which is expected to improve machinery operational reliability and safety for complicated systems or equipment, further reducing the cost of cycle life and avoiding system risk. Nowadays, most diagnostic applications are deployed based on the vibration signal, which can be conveniently acquired and contains abundant signature information that reflects the potential failures and performance degradation trend of the monitored system.

The aim of this special issue is to publish new progress with the state of the art in the various engineering applications. Prospective authors are invited to submit high-quality original contributions and reviews for this special issue, including novel theories, methodologies, and algorithms with necessary case studies in the research areas as below.

Potential topics include but are not limited to the following:

• Fault feature self-learning based on cognitive computing
• Vibration and shock measurement, signal analysis, and simulation
• Fatigue analysis for random vibrations
• Experimental modal analysis
• Vibration-based accelerated degradation testing techniques
• Structural health monitoring based on piezoelectric signals
• Vibration-based diagnosis, performance assessment, and prognostics for electromechanical systems
• Integration and verification techniques for vibration-based fault diagnosis systems