基于天牛群算法优化SVM的磨煤机故障诊断

Fault diagnosis of coal mill based on BSO-SVM

在最小二乘支持向量机基础上建立磨煤机的故障诊断模型,采用该模型进行故障诊断时,支持向量机受到核函数参数和惩罚因子的影响较大,针对这一问题,采用天牛群(BSO)算法对模型参数进行优化,提出了一种基于支持向量机(SVM)的磨煤机故障诊断方法。首先,通过引进天牛须搜索策略,对粒子群算法的位置更新规则进行了改进;然后,通过偏互信息方法对故障特征进行了筛选,结合某电厂实测数据,利用改进的算法对支持向量机核函数参数和惩罚因子进行了优化;最后,分别使用天牛群算法优化支持向量机模型(BSO-SVM)、粒子群算法优化支持向量机模型(PSO-SVM)、遗传算法优化支持向量机(GA-SVM)模型和支持向量机模型对磨煤机进行了故障诊断,并将其与实际故障类型进行了对比;对4个模型分别添加了不同级别的噪声干扰,并测试了模型的稳定性。研究结果表明:BSO-SVM模型的分类准确率最高,达到了96.88%;在5个级别的噪声干扰下,BSO-SVM模型的评价指标F1_(ave)均能够保持最高水平;与SVM、PSO-SVM、GA-SVM模型相比,BSO-SVM可以更稳定、更准确地识别故障,为磨煤机故障诊断提供实际参考。

The fault diagnosis model of coal mill was established based on least squares support vector machine.When using the model for fault diagnosis,support vector machine was greatly affected by kernel function parameters and penalty factors.To solve the problem,the model parameters were optimized by beetle swarm optimization(BSO)algorithm,and a coal mill fault diagnosis method based on support vector machine(SVM)was proposed.Firstly,the longicorn whisker search strategy was introduced to improve the position update rules of particle swarm optimization algorithm.Then,the fault characteristics were screened by partial mutual information method.Combining with the measured data of a power plant,the improved algorithm was used to optimize the kernel function parameters and penalty factors of support vector machine.Finally,BSO-SVM(beetle swarm optimization-support vector machine),SVM(support vector machine),PSO-SVM(partical swarm optimization-support vector machine)and GA-SVM(genetic algorithm-support vector machine)models were used to diagnose the fault of coal mill,which were compared with the actual fault types,and different levels of noise interference were added to the four models to test the stability of the model.The results show that the classification accuracy of BSO-SVM model is the highest,reaching 96.88%.In the five levels of noise interference,although the evaluation index F1_(ave) and accuracy Acc_(ave) of the model decreased slightly,they can still maintain the highest level.Comparing with SVM,PSO-SVM and GA-SVM models,BSO-SVM can identify faults more stably and accurately,and provide practical reference for mill fault diagnosis.