Some critical components, such as bearings, gearboxes, and impellers, are widely used in machines. Their faults may accelerate the failures of other components and finally result in machine breakdowns. To prevent any unexpected machine breakdowns and accidents, early faults of critical components should be detected as soon as possible. Once early faults of critical components are diagnosed, their performance degradation assessment and remaining useful life estimation should be conducted to maximize lifetime of critical components. This special issue focuses on vibration based methods for fault diagnosis and prognosis of critical components.

Potential topics include, but are not limited to:

• Patten recognition methods for diagnosis and prognosis of critical components
• Digital signal processing methods for diagnosis and prognosis of critical components
• Statistical signal processing methods for diagnosis and prognosis of critical components
• Reliability and robustness Bayesian methods for diagnosis and prognosis of critical components
• Soft computing and related techniques, such as evolutionary computing, fuzzy computing, probabilistic computing, and rough sets, and their new applications to diagnosis and prognosis of critical components