基于K-AHNs的轮毂电机状态识别方法研究

Condition Recognition Method Based on K-AHNs for In-wheel Motor

为规避轮毂电机故障恶化诱发电动汽车运行安全隐患,提出一种基于K-means聚类算法的改进人工碳氢网络(K-means based artificial hydrocarbon networks,K-AHNs)的轮毂电机状态识别新方法,主要通过K-means聚类算法思想改进人工碳氢网络(Artificial hydrocarbon networks,AHNs)碳氢分子区间的更新方式,优化多种状态识别模型,进而达到提高识别精度、降低训练时间的目的。基于轮毂电机内侧滚动轴承内圈、外圈和滚动体3种不同故障状态在4种负载和7种运行状态下的试验数据验证结果表明,K-AHNs法在多种运行工况下能够精准、高效地识别轮毂电机运行状态,状态识别率均大于87%,训练时间均低于19 s。比较传统的AHNs法,K-AHNs法的平均状态识别率提高了14.49%,平均训练时间缩短了7.36倍,具有较高的可靠性和实用性,较好地解决现有的电机故障诊断方法识别精度低、模型训练时间长的问题。

To avoid the potential safety hazards of electric vehicle caused by fault deterioration of in-wheel motor,a condition recognition method based on artificial hydrocarbon networks with K-means clustering algorithm(K-AHNs)is proposed.Based on the K-means clustering algorithm,the traditional updating method is improved to optimize the molecular interval and multiple condition recognition models of artificial hydrocarbon networks(AHNs),then the fault recognition accuracy is improved and the training time is significantly reduced.The experimental data of 3 bearing faults such as inner-ring fault,outer-ring fault and rolling element fault of in-wheel motor with 4 kinds of roads in 7 running conditions are used to verify the performance of the proposed method.Each condition recognition accuracy of the proposed method is greater than 87%,and each training time is less than 19 seconds.Comparing the traditional AHNs,the average recognition accuracy based on K-AHNs is increased by 14.49%,and the average training time is 5 times shorter.It has been proved that K-AHNs not only improves the condition recognition accuracy,but also reduces the training time,then K-AHNs has better reliability and practicability,which solves some problems of the existing fault diagnosis of in-wheel motor,such as low recognition accuracy and long training time.