Construction and verification of vibration signal analysis and fault prediction model for mechanical and electrical equipment
机电设备振动信号分析与故障预测模型的构建与验证

摘要

机电设备在运行过程中，振动信号蕴含着丰富的设备状态信息。通过对振动信号进行分析，可构建故障预测模型，提前发现潜在故障，减少停机时间和维修成本。本文基于振动信号特征提取与机器学习算法，构建了机电设备故障预测模型，并通过实验验证了模型的有效性。研究结果表明，该模型能够准确预测设备故障，为机电设备的智能化维护提供了理论依据和技术支持。

Abstract

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.