Journal of Electrical Engineering and Automation

During the operation of electromechanical equipment, vibration signals contain rich equipment status information. By analyzing vibration signals, a fault prediction model can be constructed to detect potential faults in advance, reducing downtime and maintenance costs. This article constructs a fault prediction model for electromechanical equipment based on vibration signal feature extraction and machine learning algorithms, and verifies the effectiveness of the model through experiments. The research results indicate that the model can accurately predict equipment failures, providing theoretical basis and technical support for the intelligent maintenance of electromechanical equipment.

Electromechanical equipment; Vibration signal analysis; Fault prediction; Feature extraction; Model validation