Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment.At present,the diagnosis of various kinds of bearing fault inform...Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment.At present,the diagnosis of various kinds of bearing fault information,such as the occurrence,location and degree of fault,can be carried out by machine learning and deep learning and realized through the multiclassification method.However,the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information.To improve the above shortcomings,an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approach-In this model,the labels of each bearing are binarized by using the binary relevance method.Then,the integrated convolutional neural network and gated recurrent unit(CNN-GRU)is employed to classify faults.Different from the general CNN networks,the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.Findings-The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model.The experimental results show that the average accuracy in test set is 99.7%,and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing,and the multilabel classification method is superior to the multiclassification method.Consequently,the model can intuitively classify faults with higher accuracy.Originality/value-The fault labels of each bearing are labeled according to the failure or not,the fault location,the damage mode and the damage degree,and then the binary value is obtained.The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method,and the predicted probability value of each fault label is directly output in the output layer,which visually distinguishes different fault conditions.展开更多
文摘由于涡扇发动机不同单元体之间存在耦合性,采用单一性能退化参数预测发动机剩余寿命明显是不全面的。本文根据风扇故障导致涡扇发动机退化机理,引入Frank Copula函数描述二元性能参数之间的相关性,并且采用二元非线性Wiener过程来构建性能退化模型,然后基于MCMC(Markov Chain Monte Carlo)方法进行模型参数估计,实现涡扇发动机剩余寿命预测。最终,通过涡扇发动机的仿真数据集来验证该方法的适用性。证明基于Copula函数的二元非线性Wiener过程建模为发动机剩余寿命预测提供了理论基础和技术支持。
基金supported by the Mountain and Sea Talents Project of Shandong University of Science and Technology(grant numbers:01040055230)the National Nature Science Foundation of Shandong Province of China(Grant No.ZR2018MEE024).
文摘Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment.At present,the diagnosis of various kinds of bearing fault information,such as the occurrence,location and degree of fault,can be carried out by machine learning and deep learning and realized through the multiclassification method.However,the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information.To improve the above shortcomings,an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.Design/methodology/approach-In this model,the labels of each bearing are binarized by using the binary relevance method.Then,the integrated convolutional neural network and gated recurrent unit(CNN-GRU)is employed to classify faults.Different from the general CNN networks,the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.Findings-The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model.The experimental results show that the average accuracy in test set is 99.7%,and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing,and the multilabel classification method is superior to the multiclassification method.Consequently,the model can intuitively classify faults with higher accuracy.Originality/value-The fault labels of each bearing are labeled according to the failure or not,the fault location,the damage mode and the damage degree,and then the binary value is obtained.The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method,and the predicted probability value of each fault label is directly output in the output layer,which visually distinguishes different fault conditions.
