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| 1 | Deep convolutional tree-inspired network:a decision-tree-structured neural network for hierarchical fault diagnosis of bearings显示文摘The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery.Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge.However,the inexplicability and low generalization ability of fault diagnosis models still bar them from the application.To address this issue,this paper explores a decision-tree-structured neural network,that is,the deep convolutional tree-inspired network(DCTN),for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network(CNN)and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree,which is by no means a simple combination of the two models.The proposed DCTN model has unique advantages in 1)the hierarchical structure that can support more accuracy and comprehensive fault diagnosis,2)the better interpretability of the model output with hierarchical decision making,and 3)more powerful generalization capabilities for the samples across fault severities.The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig.Experimental results can fully demonstrate the feasibility and superiority of the proposed method. | Xu WANG Hongyang GU Tianyang WANG Wei ZHANG Aihua LI Fulei CHU | 2021 | Frontiers of Mechanical Engineering2021，16，4： | 1 |
| 2 | 参数化时频分析在连续波有源探测中的应用显示文摘针对双曲调频信号在连续波有源探测中的目标回波检测和直达波干扰抑制问题,提出了基于参数化时频分析的连续波信号处理算法。首先,通过分析双曲调频连续波的信号时频特性,结合参数化时频分析思想,提出了适于有源探测的频域参数化时频变换和针对双曲调频信号的核函数设计。然后,为抑制接收信号中的直达波干扰,采用参数化旋转时频变换处理接收信号,并进行时频域滤波以分离信号分量,再利用反变换重构回波信号。数值仿真和海上实验结果表明,所提算法不仅能准确估计信号时频特征、有效获取双曲调频连续波信号的时频增益、提升检测性能,还可以有效抑制直达波干扰。 | 薛城 顾怡鸣 宫在晓 李整林 | 2022 | 兵工学报2022，43，7： | 0 |
| 3 | A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis显示文摘Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis. | Long WEN You WANG Xinyu LI | 2022 | Frontiers of Mechanical Engineering2022，17，2： | 0 |
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