The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics cause...The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.展开更多
鸭蛋裂纹检测技术对于禽蛋加工工厂实现智能化蛋品检测、分级具有重要意义。该研究针对鸭蛋裂纹检测流程复杂、计算量大、模型尺寸大等问题,提出了一种基于改进YOLOv5l(you only look once version5 large)的轻量裂纹检测算法,通过在黑...鸭蛋裂纹检测技术对于禽蛋加工工厂实现智能化蛋品检测、分级具有重要意义。该研究针对鸭蛋裂纹检测流程复杂、计算量大、模型尺寸大等问题,提出了一种基于改进YOLOv5l(you only look once version5 large)的轻量裂纹检测算法,通过在黑暗条件下使用LED灯照射鸭蛋,根据裂纹蛋壳与完好蛋壳透光性不同产生的图像差异进行检测。通过在YOLOv5中引入Ghost_conv模块,大大减少了模型的浮点计算量和参数量,并在模型的骨干网络中加入ECA(efficient channel attention)注意力机制以及使用多尺度特征融合方法 BIFPN(bi-directional feature pyramid network),增加模型对有效信息的关注度,以提高算法检测精度。同时使用CIoU与α-IoU损失函数融合后替代YOLOv5原始GIoU函数加速回归预测。利用自建的鸭蛋裂纹数据集验证改进后模型的性能,结果表明,本研究提出的改进YOLOv5l网络模型检测精准率为93.8%,与原始YOLOv5l模型相比,检测精度提高了6.3个百分点,参数量和浮点计算量分别减少了30.6%、39.4%。检测帧速率为28.954帧/s,较原始YOLOv5l模型仅下降3.824帧/s。与其他的目标检测常用网络SSD(single shot multibox detector)、YOLOv4、Faster-RCNN(faster region convolutional neural networks)相比,精度分别提高了13.1、12.5、8.2个百分点。本研究提出的方法能够在低硬件资源条件下进行高精度检测,可为实际场景应用提供解决方案和技术支持。展开更多
基金Supported by National Natural Science Foundation of China (Grant No.11972129)National Science and Technology Major Project of China (Grant No.2017-IV-0008-0045)+1 种基金Heilongjiang Provincial Natural Science Foundation (Grant No.YQ2022A008)the Fundamental Research Funds for the Central Universities。
文摘The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.
