将支持向量机理论引入振动机械故障诊断中,提出了新的故障诊断模型—PSO-SVM模型,并采用粒子群优化算法对支持向量机的参数进行优化,使得故障诊断结果更加准确。通过实例中的样本数据进行新模型的训练与测试,将其结果与传统神经网络相比,证明其模型具有更高的准确性。 The introduction of support vector machine theory into the vibration machine fault diagnosis, put forward a new fault diagnosis model-PSO-SVM model, and the particle swarm optimization algorithm to optimize the parameters of the support vector machine, making the fault diagnosis results more accurate. Through the training and testing of the new model through the sample data in the example, the result of the new model is proved to be more accurate than the traditional neural network.