基于灰色支持向量机的汽车制动系统故障诊断与预测

Fault Diagnosis and Forecasting for Vehicle Brake System Based on Grey Support Vector Machine

分析了灰色预测方法和支持向量机各自的优缺点,提出了将二者相结合的一种新的预测模型-灰色支持向量机预测模型。为了提高预测精度,用粒子群算法对灰色支持向量机的相关初始化参数进行优化,用优化后的模型对汽车制动系统故障进行预测与诊断。实验结果表明文章所提出的预测模型有效可靠,为提高预测精度提供了新的途径。

After analyzing the advantages and disadvantages of grey forecasting methods and support vector machine （SVM） respectively, this paper proposes a new forecasting model of grey support vector machine. In order to improve the prediction accuracy, relerent initialized parameters of gray support vector machine are optimized with PSO （Particle Swarm optimization）, then braking system failures are diagnosed and predicted with optimized model. The simulation results show that the forecasting model is effective and reliable and offers a new way to improve the forecasting accuracy.