V-ASK: Vibration Analysis System Kit

The paper proposes a new monitoring system for equipment status, an automatic classification scheme based on an Autoencoder-based machine learning together with EVM and MEMAD especially regarded to vibrating machines. EVM amplifies minimal performance variations in vibrating machines using high-resolution cameras and embedded sensors, and MEMAD amplifies the visual data collected to guarantee precise problem detection. System software is developed in Python and Tensor- Flow, the core uses the autoencoders for defect detection and unsupervised classification. The system features mobile accessibility and timely alerts support real-time monitoring dashboards hosted over the cloud that adds to operational efficiency, while cloud integration guarantees that the solution scales across different equipment types. Testing and consequent results indicate lesser operating costs and system response times and show a marked difference in vibrating machine equipment health monitoring.