This study addresses motor anomaly issues in manufacturing by developing a monitoring system based on the VMX platform and AI toolbox. It captures vibration signals, performs feature analysis and classification to provide early warnings, reducing downtime and waste. Using a bearing life dataset, the system applies time- and frequency-domain features to distinguish normal, abnormal, and failure states. A real-world case validates its benefits, including reduced labor and maintenance costs, lower energy consumption, and minimized defective products, ultimately enhancing intelligent machining efficiency.