Vibration analysis is a cornerstone of condition monitoring and reliability engineering for rotating machinery. Traditional methodologies, relying on waveform, Orbits, and spectrum interpretation, have long served as effective diagnostic tools. However, with the advent of advanced data acquisition systems and artificial intelligence (AI), there exists a significant opportunity to enhance both the accuracy and predictive power of vibration-based assessments. This paper introduces VibeAI (Vibration Intelligence for Vibration Evaluation with Artificial Intelligence), a structured methodology that integrates conventional vibration analysis steps with AI-driven diagnostics, prognostics, and business intelligence to create a next-generation condition monitoring framework.
The VibeAI methodology provides a comprehensive, scalable framework that bridges traditional vibration analysis with the future of intelligent diagnostics. By embedding AI and BI into the analysis process, organizations can transition from reactive maintenance to a true condition-based and predictive maintenance strategy, improving asset reliability, reducing unplanned downtime, and optimizing operational efficiency.