development of intelligent diagnostic software for vibration interpretation of rotating equipment

Vibration condition monitoring (vcm) and data analysis are critical for detecting faults and assessing their severity in rotating equipment as part of condition-based maintenance (cbm). this paper presents the development of an intelligent diagnostic software that integrates rule-based models with machine learning algorithms to detect, analyze, and classify faults in rotating equipment from the vibration records. the proposed system includes a comprehensive graphical user interface (gui) that visualizes diagnostic results, provides actionable insights, and offers repair recommendations. by enhancing fault diagnosis accuracy and interpretability, this software aims to reduce downtime and maintenance costs for industrial applications.