基于ISCNN-LightGBM的轴承故障诊断

在传统卷积神经网络与分类器相结合的故障诊断方法中, CNN用于故障特征提取时, 存在着提取的特征质量不高与运行时间较长的问题. 针对以上问题, 本文提出了一种基于改进单层卷积神经网络及LightGBM的故障诊断模型. 该模型通过将特征距离函数嵌入CNN的损失函数中, 提升了CNN特征提取的能力, 增强了CNN与后续分类器之间的联系, 从而提升了整体模型的故障诊断能力. 于此同时, 经过改进的单层的卷积神经网络进一步缩短了模型运行的时间, 提升了模型的诊断效率. 通过对两个不同的公共数据集进行对比实验, 其结果表明, 本文所提诊断模型对多种轴承故障的诊断准确率与诊断效率显著高于其他诊断模型.     

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

As a representative deep learning network, Convolutional Neural Network (CNN) has been extensively used in bearing fault diagnosis and many good results have been reported. In Prognostics and Health Management (PHM) field, the CNN’s input size is usually designed as a 1D vector or 2D square matrix, and the convolution kernel size is also defined as a square shape like 3 × 3 and 5 × 5, which are directly adopted from the image recognition. Though satisfying results can be obtained, CNN with such parameter specifications is not optimal and efficient. To this end, this paper elaborated the physical characteristics of bearing acceleration signals to guide the CNN design. First, the fault period under different fault types and shaft rotation frequency were used to determine the size of CNN’s input. Next, an exponential function was involved in fitting the envelope of decaying acceleration signal during each fault period, and signal length within different decaying ratios was used to define the CNN’s kernel size. Finally, the designed CNN was validated with the Case Western Reserve University bearing dataset and Paderborn University bearing dataset. Results confirm that the physics-guided CNN (PGCNN) with rectangular input shape and rectangular convolution kernel works better than the baseline CNN with higher accuracy and smaller uncertainty. The feasibility of designing CNN parameters with physics-guided rules derived from bearing fault signal analysis has also been verified.     

A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems

Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of complex systems due to their superiority in feature extraction. However, existing techniques rarely consider time delay of occurrence of faults, which affects the performance of fault diagnosis. In this paper, by synthetically considering feature extraction and time delay of occurrence of faults, we propose a novel fault diagnosis method that consists of two parts, namely, sliding window processing and CNN-LSTM model based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, samples obtained from multivariate time series by the sliding window processing integrates feature information and time delay information. Then, the obtained samples are fed into the proposed CNN-LSTM model including CNN layers and LSTM layers. The CNN layers perform feature learning without relying on prior knowledge. Time delay information is captured with the use of the LSTM layers. The fault diagnosis of the Tennessee Eastman chemical process is addressed, and it is verified that the predictive accuracy and noise sensitivity of fault diagnosis can be greatly improved when the proposed method is applied. Comparisons with five existing fault diagnosis methods show the superiority of the proposed method.

     

基于Gibbs抽样的轴承故障诊断方法

针对现有轴承故障诊断方法的不足,即诊断片面性问题,提出了一种基于Gibbs抽样的轴承故障诊断方法。首先对轴承振动信号进行局部特征尺度分解（LCD）得到内禀尺度分量（ISC）;然后对轴承振动信号和ISC分别提取时域特征,按照特征敏感度高低对时域特征排名,选择排名靠前的特征组成特征集;其次使用特征集训练产生基于Gibbs抽样的多维高斯分布模型;最后通过后验分析得到概率,实现轴承故障诊断。实验结果表明诊断正确率达到100%,与基于SVM的轴承诊断方法相比,在特征数为43个时诊断正确率提升了11.1个百分点。所提方法能够有效地对滚动轴承故障状态进行诊断,对高维复杂的轴承故障数据也有很好的诊断效果。     

基于SVD-AR模型与VPMCD的轴承故障诊断方法

针对强噪声背景下振动信号故障特征难以提取的问题,提出了基于奇异值分解的自回归(SVD-AR)模型,用于提取振动信号的特征,并与变量预测模型模式识别(VPMCD)方法相结合应用于轴承故障诊断.对轴承振动信号进行SVD;然后,利用奇异值差分谱对分量信号进行筛选,对能够反映故障信息的分量信号建立AR模型,提取轴承振动信号的特征信息;采用VPMCD对滚动轴承运行状态进行识别.实验证明了方法的合理性和有效性.     

基于卷积神经网络的滚动轴承故障诊断方法

为了简单、准确地进行轴承故障诊断,结合深度学习理论,对基于卷积神经网络的滚动轴承故障诊断方法进行了研究;首先,选用了结构相对简单的LeNet5卷积神经网络;然后,对轴承振动信号原始数据进行截取和归一化处理后直接生成生成二维矩阵作为神经网络输入;接着,优选卷积核大小、批大小、学习率及迭代次数等网络模型参数;最后,应用sigmoid函数进行多标签分类;实验结果表明,该方法能有效识别正常状态及不同损伤程度下的内圈、外圈、滚动体故障状态,识别准确率达到99.50%以上水平;基于卷积神经网络的滚动轴承故障诊断方法不仅在一定程度上可以简化故障诊断的过程,而且可以充分利用卷积神经网络模型的优势实现高效准确地故障诊断。     

Induction motor fault detection and diagnosis using a current state space pattern recognition

In the last few decades the continuous monitoring of complex dynamic systems has become an increasingly important issue across diverse engineering areas. This paper presents a pattern recognition based system that uses visual-based efficient invariants features for continuous monitoring of induction motors. The procedures presented here are based on the image identification of the 3-D current state space patterns that allow the identification of distinct fault types and, furthermore, their corresponding severity. This automatic fault detection system deals with time-variant electric currents and is based on the identification of three-phase stator currents specified patterns. Several simulation and experimental results are also presented in order to verify the effectiveness of the proposed methodology.     

基于改进阈值和小波包的轴承故障诊断方法

为了准确有效地确定滚动轴承的故障部位,提出一种轴承故障诊断的新方法。用改进的小波阈值法对轴承振动信号进行降噪处理,对去噪后的信号进行小波包分解与重构,提取各重构子带内的信号特征作为故障诊断的样本,依据各子带信号的能量分布特征判断轴承的故障部位。在MATLAB环境下对SKF6205-2RS轴承的典型故障进行了仿真研究,结果表明改进的阈值法相比于传统去噪方法有较好的去噪效果,小波包能够准确提取信号的故障特征,能够提高轴承故障检测的准确性和有效性。     

基于动态卷积多层域自适应的轴承故障诊断

现有基于深度学习的轴承故障诊断方法对数据具有一定的依赖性,要求训练数据与测试数据具有相同的分布。在变工况的条件下,网络模型的故障诊断精度会因数据分布发生变化而下降。为保证网络模型能够在变工况条件下对轴承的健康状态进行准确识别,基于无监督域自适应理论,提出一种新颖的智能故障诊断网络模型——动态卷积多层域自适应网络。该网络一方面充分利用动态卷积强有力的特征提取能力,提取更多有效的故障特征;另一方面采用相关对齐实施非线性变换,同时对齐多层故障特征分布的二阶统计量,促进源域的诊断知识向目标域迁移,提高了模型在目标域无故障标签条件下的故障识别准确率。最后,在两个数据集共14个迁移任务下进行实验,实验结果表明,动态卷积多层域自适应网络能够实现较高的故障诊断识别精度。     

基于递归图和局部非负矩阵分解的轴承故障诊断

针对轴承振动信号的非平稳特征和现实中难以提取故障参数的情况,提出了一种基于递归图和局部非负矩阵分解的轴承故障诊断方法。该方法首先对采集到的轴承振动信号进行递归图分析,生成灰度图;然后用局部非负矩阵分解对生成的递归图进行特征参数提取,得到系数编码矩阵;最后采用分类器对上述编码矩阵直接进行模式识别,从而实现轴承故障的自动化诊断。将该方法应用在4种典型工况的轴承故障诊断实例中,应用结果表明,该方法可对不同工况的递归图自适应地计算特征参数,避免了人为因素对诊断准确率的影响,具有较好的自适应性和鲁棒性。     

A cascaded CNN model for multiple human tracking and re-localization in complex video sequences with large displacement

Multimedia Tools and Applications - Human tracking and localization play a crucial role in many applications like accident avoidance, action recognition, safety and security, surveillance and crowd...     

Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM

With the development of industry and technology, mechanical systems’ safety has strong relations with the diagnosis of bearing faults. Accurate fault diagnosis is essential for the safe and stable operation of rotating machinery. Most former research depends too much on the fault signal specificity and learning model’s choices. To overcome the disadvantages of lacking intrinsic mode function (IMF) modal aliasing, low degree of discrimination between data of different fault types, high computational complexity. This paper proposes a method that combines multi-scale weighted entropy morphological filtering (MWEMF) signal processing and bidirectional long-short term memory neural networks (Bi-LSTM). The developed rolling bearing fault diagnosis strategy is then implemented to different databases and potential models to demonstrate the greatly improved system’s ability to reconstruct the time-to-frequency domain characteristics of fault signature signals and reduce learning cost. After verification, the classification accuracy of the proposed model reaches 99%.

     

基于蝙蝠算法优化相关向量机的轴承故障诊断方法

由于轴承振动信号具有复杂性和非线性,难以有效提取故障特征,影响故障诊断的准确率.为了提高故障诊断准确率,提出一种蝙蝠算法(BA)优化相关向量机(RVM)的轴承故障诊断方法.首先结合变分模态分解和多尺度熵从轴承振动信号中提取出故障特征,作为相关向量机的输入向量;接着采用蝙蝠算法优化相关向量机的核函数参数;然后训练相关向量...     

基于条件局部均值分解与变量预测模型的轴承故障诊断方法

针对局部均值分解(LMD)方法在分解非线性、非平稳振动信号过程中存在的模态混淆现象,从而影响故障识别准确性的问题,提出了基于条件局部均值分解方法(CLMD)与模式识别变量预测模型(VPMCD)的故障诊断方法。该方法将数字图像处理的频率分辨率方法与LMD相结合,首先确定振动信号中所有局部极值点的频率分辨率,将振动信号分为低频率分辨率区域和高频率分辨率区域;然后对高频率分辨率区域进行LMD分解,可得若干乘积函数(PF)分量;最后用折线将所有PF分量连接起来,经滑动平均处理可得PF分量,提取PF分量的偏度系数和能量系数构成故障特征向量,用于VPMCD故障识别。将该方法应用于轴承故障诊断,实验结果表明,与LMD方法相比,识别效率提高了8.33%,表明了该方法的有效性和可行性。     

Robust adaptive control for a class of cascaded nonlinear systems with applications to space interception

This paper proposes a robust H _∞ ‐based adaptive backstepping control scheme for the output stabilization of a special class of cascaded nonlinear systems. This kind of systems possess the feature that the first sub‐equation is a linear perturbed system, whereas the rest ones perform a general semi‐strict feedback form. Different from the conventional backstepping design approach, the special cascaded structure ensures to introduce the H _∞ technique to the backstepping procedure such that both the robust performance and the robust stability can be simultaneously guaranteed. Within the Lyapunov framework, the proposed control scheme is proved to guarantee (i) the uniformly ultimate boundedness of the system signals with a bound that can be made arbitrarily small by suitably choosing control parameters; (ii) asymptotic output stabilization as long as the uncertain nonlinearities and external disturbances vanish; and (iii) ‐performance of the closed‐loop system. A space interception scenario is utilized to demonstrate the effectiveness of the proposed control scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

Internal representation of a process,fault diagnosis and fault correction

This paper reports on an investigation into the relationship between the internal representation of a process on the one hand and on the other, control behaviour when diagnosing and correcting faults. The subjects were 87 process operator trainees, performing certain tasks in a simulated process control situation. Two modes of internal representation are distinguished: a more verbal or abstract mode of the functioning of the process (the mental model) and a more visual or concrete mode of the structure of the process (the mental image). It is concluded that the mental model probably plays an important role in fault correction and in the verification process in diagnosing faults, while the mental image seems to play an important role in the search for information in the process of diagnosis. Some implications for operator training are discussed.     

基于自学习采样粒子滤波器的不完备故障空间交互诊断方法

针对不完备空间混合系统,提出一种基于自学习采样粒子滤波器(SLSPF)的交互诊断方法.融入自学习采样机制,利用自学习即时概率指导采样,以摆脱粒子滤波器对转移概率的依赖;结合自学习采样与诊断的动态交互方式调整模式空间,使粒子滤波器采样粒子数动态减少;同时给出了不完备信息空间的真实模式与未知模式阈值的决策条件.实验结果表明,尤其在高维状态空间下,SLSPF不仅可以保证粒子滤波器的诊断精度,而且能够提高计算效率.     

Improved sparse representation based on local preserving projection for the fault diagnosis of multivariable system

Dear editor, To satisfy the increasing requirements for safety and qual-ity in industrial processes,process monitoring has been ac-tively investigated in the pa...     

An intelligent fault diagnosis method for rotor-bearing system using small labeled infrared thermal images and enhanced CNN transferred from CAE

Despite deep learning models can largely release the pressure of manual feature engineering in intelligent fault diagnosis of rotor-bearing systems, their performance mostly depends on enough labeled samples constructed from the vibration signals. Acquiring lots of labeled samples is often laborious, and the vibration sensors tightly fixed on the equipment may influence their structures after long time running. To address these two problems, a new framework based on small labeled infrared thermal images and enhanced convolutional neural network (ECNN) transferred from convolutional auto-encoder (CAE) is proposed. First, infrared thermal images are measured to characterize various health states of rotor-bearing system. Second, exponential linear unit (ELU) and stochastic pooling (SP) are used to construct ECNN. Then, the model parameters of a CAE pre-trained with unlabeled thermal images are transferred to initialize the ECNN. Finally, small labeled thermal images are used for training ECNN to further adjust model parameters. The collected thermal images are used to test the diagnosis performance of the proposed method. The analysis and comparison results show that the proposed method outperforms the current mainstream methods.