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1.
Rolling bearing tips are often the most susceptible to electro-mechanical system failure due to high-speed and complex working conditions, and recent studies on diagnosing bearing health using vibration data have developed an assortment of feature extraction and fault classification methods. Due to the strong non-linear and non-stationary characteristics, an effective and reliable deep learning method based on a convolutional neural network (CNN) is investigated in this paper making use of cognitive computing theory, which introduces the advantages of image recognition and visual perception to bearing fault diagnosis by simulating the cognition process of the cerebral cortex. The novel feature representation method for bearing data is first discussed using supervised deep learning with the goal of identifying more robust and salient feature representations to reduce information loss. Next, the deep hierarchical structure is trained in a robust manner that is established using a transmitting rule of greedy training layer by layer. Convolution computation, rectified linear units, and sub-sampling are applied for weight replication and reducing the number of parameters that need to be learned to improve the general feed-forward back propagation training. The CNN model could thus reduce learning computation requirements in the temporal dimension, and an invariance level of working condition fluctuation and ambient noise is provided by identifying the elementary features of bearings. A top classifier followed by a back propagation process is used for fault classification. Contrast experiments and analyses have been undertaken to delineate the effectiveness of the CNN model for fault classification of rolling bearings. 相似文献
2.
《Displays》2021
Arterial spin labeling (ASL) is a relatively new MRI technique that can measure cerebral blood flow, which is of great importance for the diagnosis of dementia diseases. Besides, this valuable imaging modality does not need exogenous tracers and has no radiation, which makes it favorable for elder patients. However, ASL data does lack in many contemporary image-based dementia diseases datasets, which include popular ADNI-1/GO/2/3 datasets. In order to supplement the valuable ASL data, a new Generative adversarial network (GAN)-based model is proposed to synthesize ASL images in this study. This new model is unique, as the popular variational auto-encoder (VAE) has been utilized as the generator of the GAN-based model. Hence, a new VAE-GAN architecture is introduced in this study. In order to demonstrate its superiority, dozens of experiments have been conducted. Experimental results demonstrate that, this new VAE-GAN model is superior to other state-of-the-art ASL image synthesis methods, and the accuracy improvement after incorporating synthesized ASL images from the new model can be as high as 42.41% in dementia diagnosis tasks. 相似文献
3.
基于混沌动力学的滚动轴承故障诊断研究 总被引：3，自引：0，他引：3
为了研究旋转机械非平稳信号的非线性动力学特征,探索旋转机械故障诊断方法,以滚动轴承为研究对象,采用嵌入式传感器获取滚动轴承振动信号,通过计算滚动轴承振动时间序列的关联维数、Kolmogorov熵等混沌特征量,提取设备运动状态特征,并对其进行了详细分析。结果表明：该方法可以实现对滚动轴承的故障诊断,从而为旋转机械的故障诊断提供了一种新的方法。 相似文献
4.
Bearing fault diagnosis plays an important role in rotating machinery equipment’s safe and stable operation. However, the fault sample collected from the equipment is seriously absent, which obstacles the establishment of the diagnostic model. In this paper, a novel false-real data synthesis method combined bearing dynamic model with a generated adversarial network is proposed to solve the problem of zero-shot in new condition. Firstly, the bearing dynamic model is constructed to simulate vibration data in different conditions. Secondly, the conversion model is trained by simulation data in different conditions, which will be employed to convert real-world data in the old condition into the conversion data in the new condition. Thirdly, the GAN model is trained by simulation data and real-world data in old condition and finetuned by simulation data and conversion data in the new condition. Finally, simulation data in the new condition are inputted to the finetuned GAN model to obtain generated data in the new condition, and the fault diagnosis model is trained by it. To validate the performance of the proposed method, various comparative experiments are carried out on one rolling bearing dataset. The results indicate that the proposed method can solve the problem of zero-shot in new condition with excellent diagnosis performance. 相似文献
5.
赵一瑾 《计算机测量与控制》2021,29(9):23-28
针对当前诊断方法对滚动轴承故障特征表征困难以及在噪声干扰大的环境中检测性能下降的问题,提出了一种基于加权密集连接网络和注意力机制的滚动轴承故障诊断的方法,该方法由特征提取和故障分类两部分组成;在特征提取部分,首先采用加权密集连接网络从轴承振动信号中提取特征,并将不同空间级别的特征进行组合以增强信息的多样性,然后利用注意力机制突出重要信息,获得准确的表征故障特征;故障分类模型以表征的特征信息作为输入,经过Softmax函数输出每种故障类型的诊断结果;实验结果表明,所提模型在加性噪声干扰的情况下具有良好的诊断性能,比其他方法更具优势。 相似文献
6.
基于软件共振解调分析的滚动轴承故障诊断系统开发 总被引：5，自引：0，他引：5
滚动轴承是机械设备的重要组成部分,很大一部分的机械故障都是由它引起的,共振解调是一种有效的分析滚动轴承故障信号的方法,本文使用办报编程实现共振解调,利用Labwiew软件,在完全依靠软件信号处理方法的基础上,成功实现了故障信息的提取,降低了系统的成本,提高了系统的实用性。 相似文献
7.
Considering all the monitoring data of bearings until failure, very few data are acquired when the bearings are faulty. Such circumstance leads to small faulty sample problem when an intelligent fault diagnosis method is applied. A deep neural network trained with small samples cannot be trained completely, and tends to overfit, which results in poor performance in practical application. To solve this problem, a compact convolutional neural network augmented with multiscale feature extraction is proposed in this paper. Multiscale feature extraction unit is introduced to extract features at different time scales without adding convolution layers, which can reduce the depth of the network while ensuring classification ability and alleviating the overfitting problem caused by the network being too complicated. Besides, a specially designed compact convolutional neural network synthetically analyzes the multiscale features. By combing these two tricks, the proposed neural network can extract more sensitive features with a relatively shallow structure, which increases classification accuracy under small samples. Dropout technique is also used to prevent the network from overfitting. Effectiveness of the proposed method is verified by three bearing datasets. Experiments show that this network can achieve competitive results with limited training samples even with different load and mixed rotating speed. 相似文献
8.
针对滚动轴承在故障诊断过程中信号特征提取困难导致诊断准确率低、鲁棒性差的问题,提出一种基于Squeeze-Excitation-ResNeXt(SE-ResNeXt)网络的滚动轴承故障诊断方法;将采集的一维轴承振动信号作为输入,进行滑动窗口采样与标准化处理,通过压缩、激励操作进行特征重标定,扩大模型感受野,并级联聚集残差变换网络自适应提取故障信号特征;在模型训练过程中选择最优压缩率为1/8以及8个组卷积,引入Relu函数加快网络收敛,使用全局平均池化替代全连接层避免过拟合现象,构造能够自主进行表征学习的最优故障诊断模型;通过仿真实验表明:与目前的深度学习算法相比,SE-ResNeXt网络能够准确的实现轴承故障诊断,并在高噪声的环境下仍具有较好的鲁棒性。 相似文献
9.
Fault diagnosis of rolling bearing is crucial for safety of large rotating machinery. However, in practical engineering, the fault modes of rolling bearings are usually compound faults and contain a large amount of noise, which increases the difficulty of fault diagnosis. Therefore, a deep feature enhanced reinforcement learning method is proposed for the fault diagnosis of rolling bearing. Firstly, to improve robustness, the neural network is modified by the Elu activation function. Secondly, attention model is used to improve the feature enhanced ability and acquire essential global information. Finally, deep Q network is established to accurately diagnosis the fault modes. Sufficient experiments are conducted on the rolling bearing dataset. Test result shows that the proposed method is superior to other intelligent diagnosis methods. 相似文献
10.
This paper presents an intelligent diagnosis method for a rolling element bearing; the method is constructed on the basis of possibility theory and a fuzzy neural network with frequency-domain features of vibration signals. A sequential diagnosis technique is also proposed through which the fuzzy neural network realized by the partially-linearized neural network (PNN) can sequentially identify fault types. Possibility theory and the Mycin certainty factor are used to process the ambiguous relationship between symptoms and fault types. Non-dimensional symptom parameters are also defined in the frequency domain, which can reflect the characteristics of vibration signals. The PNN can sequentially and automatically distinguish fault types for a rolling bearing with high accuracy, on the basis of the possibilities of the symptom parameters. Practical examples of diagnosis for a bearing used in a centrifugal blower are given to show that bearing faults can be precisely identified by the proposed method. 相似文献
11.
传统的小波分解存在小波基函数难以选择的问题,经验模式分解（EMD）存在模式混叠现象,不能准确地诊断出轴承故障。因此本文将集成经验模式分解（EEMD）和能量算子解调相结合,建立了滚动轴承单自由度模型,仿真故障激励,以获取轴承故障动力学响应。最后利用模型结果对本文方法进行数值验证,以证明本文所提方法的有效性和正确性。 相似文献
12.
低秩纹理结构是图像处理领域中具有重要几何意义的结构,通过提取低秩纹理可以对受到各种变换干扰的图像进行有效校正.针对受到各种变换干扰的低秩图像校正问题,利用生成式框架来缓解图像中不具明显低秩特性区域的校正结果不理想的问题,提出了一种非监督式的由图像生成图像的低秩纹理生成对抗网络（Low-rank generative adversarial network,LR-GAN）算法.首先,该算法将传统的无监督学习的低秩纹理映射算法（Transform invariant low-rank textures,TILT）作为引导加入到网络中来辅助判别器,使网络整体达到无监督学习的效果,并且使低秩对抗对在生成网络和判别网络上都能够学习到结构化的低秩表示.其次,为了保证生成的图像既有较高的图像质量又有相对较低的秩,同时考虑到低秩约束条件下的优化问题不易解决（NP难问题）,在经过一定阶段TILT的引导后,设计并加入了低秩梯度滤波层来逼近网络的低秩最优解.通过在MNIST,SVHN和FG-NET这三个数据集上的实验,并使用分类算法评估生成的低秩图像质量,结果表明,本文提出的LR-GAN算法均取得了较好的生成质量与识别效果. 相似文献
13.
武器系统的效能评估受很多因素的影响,神经网络是现代武器系统效能评估的重要方法,但受样本量的限制,很难达到预期的训练效果。针对这一问题,选取少批量真实数据训练生成对抗网络,待网络达到纳什均衡后,利用生成网络产生同分布的伪数据。将伪数据与真实数据结合形成扩增样本,使用扩增样本训练深度神经网络用以评估。同时,生成对抗网络中的判别网络也能为专家评估提供一定的参考。 相似文献
14.
In view of the difficulty in measuring the speed signal and integrating the vibration and speed information flexibly in actual variable speed bearing fault diagnosis, a single vibration signal-driven variable speed intelligent fault diagnosis scheme for rolling bearings is developed to guarantee the reliability and safety of the equipment in this paper. In the proposed fault diagnosis scheme, the extreme multi-scale entropy (EMSEn) of the raw vibration signal is employed as the alternative characterization parameter of the speed information, and an intelligent diagnosis model named deep branch attention network (DBANet) is developed to integrate the vibration and speed information more flexibly. The developed DBANet contains 2 parallel and relatively independent forward propagation channels, and the attention mechanism is introduced into the deep architecture at branch level to adjust the importance of different branches, which endow the model with the ability of fusing the vibration and speed information autonomously. The effectiveness of the proposed method is verified by experiments, and the experimental results show that, compared with the methods relying on external information fusion, the suggested DBANet can integrate the vibration and speed information more flexibly. Besides, in the case of no speed signal, the proposed diagnosis scheme can achieve more outstanding results compared with the methods of using other multi-scale entropy features as the alternative characterization parameter of the speed information. 相似文献
15.
Fault diagnosis with transfer learning has achieved great attention. However, existing methods mostly focused on single-source-single-target sceneries. In some cases, there may exist multiple source domains. Therefore, a reinforcement ensemble deep transfer learning network (REDTLN) is proposed for fault diagnosis with multi-source domains. Firstly, various new kernel maximum mean discrepancies (kMMDs) are used to construct multiple deep transfer learning networks (DTLNs) for single-source-single-target domain adaptation. The differences of kernel functions and source domains can help the DTLNs learn diverse transferable features. Secondly, a new unified metric is designed based on kMMD and diversity measures for unsupervised ensemble learning. Finally, using the unified metric as the reward, a reinforcement learning method is firstly explored to generate an effective combination rule for multi-domain-multi-model reinforcement ensemble. The proposed method is verified with experiment datasets, and the results empirically show its effectiveness and superiority compared with other methods. 相似文献
16.
对现有的滚动轴承故障检测仪进行了调查和研究,设计了一种功耗小、成本低的便携式滚动轴承故障检测仪。该检测仪硬件结构及软件系统基于MSP430F149单片机设计与开发,采用时域参数指标诊断滚动轴承故障。实测结果表明,检测仪能快速、自动地诊断滚动轴承故障,验证了该设计的有效性和可靠性。 相似文献
17.
Fault source diagnosis methodology is one of the key technologies of quality control and assurance for multi-source & multi-stage manufacturing processes, especially in small sample manufacturing systems. By analyzing the existing research on fault source diagnosis methods, a Bayesian network-based methodology is proposed. Gray correlation theory and mechanism analysis method are used in the process of Bayesian network model construction to reduce the dependence of sample data size for structure learning in the process of small sample manufacturing of complex products. In addition, two fault source diagnosis methods based on manufacturing principle analysis and reverse Bayesian network respectively are proposed. The strategy of the combined use of the two methods in the actual manufacturing scenes is given to cope with the fault source diagnosis scenario in the real manufacturing process. In the end, an example from an actual factory is provided to validate the effectiveness and efficiency of the proposed model and methodologies. 相似文献
18.
图像攻击是指通过对图像添加小幅扰动使深度神经网络产生误分类。针对现有图像攻击算法在变分自编码器（VAE）防御下攻击性能不稳定的问题,在AdvGAN算法的基础上,提出基于对抗机制的AntiVAEGAN算法获取对VAE防御的稳定攻击效果。为应对AntiVAEGAN算法防御能力提升时攻击性能不稳定的问题,结合生成器与鉴别器、生成器与VAE的双对抗机制提出改进的图像攻击算法VAEAdvGAN。在MNIST和GTSRB数据集上的实验结果表明,在无防御的情况下,AntiVAEGAN和VAEAdvGAN算法几乎能达到与AdvGAN算法相同的图像分类准确率和攻击成功率,而在VAE防御的情况下,VAEAdvGAN相比AdvGAN和AntiVAEGAN算法整体攻击效果更优。 相似文献
19.
20.
《Expert systems with applications》2014,41(7):3391-3401
Rolling element bearings play an important role in ensuring the availability of industrial machines. Unexpected bearing failures in such machines during field operation can lead to machine breakdown, which may have some pretty severe implications. To address such concern, we extend our algorithm for solving trace ratio problem in linear discriminant analysis to diagnose faulty bearings in this paper. Our algorithm is validated by comparison with other state-of art methods based on a UCI data set, and then be extended to rolling element bearing data. Through the construction of feature data set from sensor-based vibration signals of bearing, the fault diagnosis problem is solved as a pattern classification and recognition way. The two-dimensional visualization and classification accuracy of bearing data show that our algorithm is able to recognize different bearing fault categories effectively. Thus, it can be considered as a promising method for fault diagnosis. 相似文献