摘要
针对传统滚动轴承故障诊断方法训练时间长和效率低的问题,提出一种基于卷积神经网络(convolutional neural networks,简称CNN)和宽度学习系统(broad learning system,简称BLS)的故障诊断方法,实现了端到端的快速准确模式识别.首先,建立CNN与BLS结合的宽度卷积学习系统(broad convolutional learning system,简称BCLS),利用CNN提取信号特征和BLS进行分类,获得系统输出;其次,通过残差学习增加BLS层数,形成堆叠宽度卷积学习系统(stacked broad convolutional learning system,简称SBCLS),优化预测输出与真实标签的误差,对轴承故障模式进行识别;最后,通过试验将所提方法与3种BLS方法的预测结果进行了比较验证.结果表明,与几种常见故障诊断方法相比,所提方法诊断效果更佳,具有更高的准确率和训练效率,在边缘端的智能故障诊断中具有较好的应用前景.
Abstract
To address the issue of long training time and low efficiency in traditional rolling bearing fault diagno-sis methods,a fault diagnosis method based on convolutional neural networks(CNN)and broad learning sys-tem(BLS)is proposed to realize fast and accurate end-to-end pattern recognition.A broad convolutional learn-ing system(BCLS)is established by combining CNN and BLS,using CNN to extract signal features and BLS for classification to generate system output.BLS layers are integrated through residual learning to form a stacked broad convolutional learning system(SBCLS),which optimize the error between predicted outputs and real la-bels,thereby recognizing bearing fault patterns.Control experiments are set up to verify the proposed method.A comparative test with three BLS methods indicate that the proposed method offers superior diagnostic perfor-mance.In addition,when compared to several common fault diagnosis methods,the proposed method demon-strates higher accuracy and training efficiency,showing promise for intelligent fault diagnosis at the edge.
基金项目
山东省自然科学基金资助项目(ZR2019PEE018)
山东省自然科学基金资助项目(ZR2020QE158)
山东省科技型中小企业创新能力提升资助项目(2021TSGC1063)
青岛市自然科学基金资助项目(23-2-1-216-zyydjch)
NETL
NSTL
万方数据