School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Bosch Automotive Products (Suzhou) Co., Ltd., Suzhou 215021, China.
Sensors (Basel). 2022 Aug 22;22(16):6316. doi: 10.3390/s22166316.
To avoid the potential safety hazards of electric vehicles caused by the mechanical fault deterioration of the in-wheel motor (IWM), this paper proposes an intelligent diagnosis based on double-optimized artificial hydrocarbon networks (AHNs) to identify the mechanical faults of IWM, which employs a K-means clustering and AdaBoost algorithm to solve the lower accuracy and poorer stability of traditional AHNs. Firstly, K-means clustering is used to improve the interval updating method of any adjacent AHNs molecules, and then simplify the complexity of the AHNs model. Secondly, the AdaBoost algorithm is utilized to adaptively distribute the weights for multiple weak models, then reconstitute the network structure of the AHNs. Finally, double-optimized AHNs are used to build an intelligent diagnosis system, where two cases of bearing datasets from Paderborn University and a self-made IWM test stand are processed to validate the better performance of the proposed method, especially in multiple rotating speeds and the load conditions of the IWM. The double-optimized AHNs provide a higher accuracy for identifying the mechanical faults of the IWM than the traditional AHNs, K-means-based AHNs (K-AHNs), support vector machine (SVM), and particle swarm optimization-based SVM (PSO-SVM).
为避免轮边电机（IWM）机械故障劣化导致的电动汽车潜在安全隐患，本文提出了一种基于双重优化人工碳氢网络（AHN）的智能诊断方法，以识别 IWM 的机械故障，该方法采用 K-means 聚类和 AdaBoost 算法解决了传统 AHN 精度较低和稳定性较差的问题。首先，K-means 聚类用于改进任意相邻 AHN 分子的区间更新方法，然后简化 AHN 模型的复杂性。其次，利用 AdaBoost 算法自适应地分配多个弱模型的权重，然后重新构建 AHN 的网络结构。最后，采用双重优化的 AHN 构建智能诊断系统，处理来自帕德博恩大学的两个轴承数据集案例和自制的 IWM 试验台，验证所提出方法的更好性能，尤其是在多种转速和 IWM 的负载条件下。与传统的 AHN、基于 K-means 的 AHN（K-AHN）、支持向量机（SVM）和基于粒子群优化的 SVM（PSO-SVM）相比，双重优化的 AHN 为识别 IWM 的机械故障提供了更高的准确性。
