Deep Transfer Network with Multi-Space Dynamic Distribution Adaptation for Bearing Fault Diagnosis

Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis. To address the above problems, we propose a deep convolutional multi-space dynamic distribution adaptation (DCMSDA) model, which consists of two core components: two feature extraction modules and a dynamic distribution adaptation module. Technically, a multi-space structure is proposed in the feature extraction module to fully extract fault features of the marginal distribution and conditional distribution. In addition, the dynamic distribution adaptation module utilizes different metrics to capture distribution discrepancies, as well as an adaptive coefficient to dynamically measure the alignment proportion in complex cross-domain scenarios. This study compares our method with other advanced methods, in detail. The experimental results show that the proposed method has excellent diagnosis performance and generalization performance. Furthermore, the results further demonstrate the effectiveness of each transfer module proposed in our model.     

A Novel Method for Fault Diagnosis of Rotating Machinery

When rotating machinery fails, the consequent vibration signal contains rich fault feature information. However, the vibration signal bears the characteristics of nonlinearity and nonstationarity, and is easily disturbed by noise, thus it may be difficult to accurately extract hidden fault features. To extract effective fault features from the collected vibration signals and improve the diagnostic accuracy of weak faults, a novel method for fault diagnosis of rotating machinery is proposed. The new method is based on Fast Iterative Filtering (FIF) and Parameter Adaptive Refined Composite Multiscale Fluctuation-based Dispersion Entropy (PARCMFDE). Firstly, the collected original vibration signal is decomposed by FIF to obtain a series of intrinsic mode functions (IMFs), and the IMFs with a large correlation coefficient are selected for reconstruction. Then, a PARCMFDE is proposed for fault feature extraction, where its embedding dimension and class number are determined by Genetic Algorithm (GA). Finally, the extracted fault features are input into Fuzzy C-Means (FCM) to classify different states of rotating machinery. The experimental results show that the proposed method can accurately extract weak fault features and realize reliable fault diagnosis of rotating machinery.     

A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.     

旋转机械振动故障诊断专家系统

针对旋转机械故障诊断智能化程度不高的现状,采用可视化语言LabVIEW作为故障诊断专家系统前端开发平台,采用MySQL作为系统后台数据库,开发了一个基于产生式规则表示知识的旋转机械故障诊断专家系统。介绍了该系统的总体结构、知识库、推理机的设计。最后给出该专家系统的实例验证,证明了该方法的实用性。     

Improved Variational Mode Decomposition and CNN for Intelligent Rotating Machinery Fault Diagnosis

This paper proposes an intelligent diagnosis method for rotating machinery faults based on improved variational mode decomposition (IVMD) and CNN to process the rotating machinery non-stationary signal. Firstly, to solve the problem of time-domain feature extraction for fault diagnosis, this paper proposes an improved variational mode decomposition method with automatic optimization of the number of modes. This method overcomes the problems of the traditional VMD method, in that each parameter is set by experience and is greatly influenced by subjective experience. Secondly, the decomposed signal components are analyzed by correlation, and then high correlated components with the original signal are selected to reconstruct the original signal. The continuous wavelet transform (CWT) is employed to extract the two-dimensional time–frequency domain feature map of the fault signal. Finally, the deep learning method is used to construct a convolutional neural network. After feature extraction, the two-dimensional time-frequency image is applied to the neural network to identify fault features. Experiments verify that the proposed method can adapt to rotating machinery faults in complex environments and has a high recognition rate.     

A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets

At present, the success of most intelligent fault diagnosis methods is heavily dependent on large datasets of artificial simulation faults (ASF), which have not been widely used in practice because it is often costly to obtain a large number of samples in reality. Fortunately, various faults can be easily simulated in the laboratory, and these simulated faults contain a lot of fault diagnosis knowledge. In this study, based on a Siamese network framework, we propose a bearing fault diagnosis based on few-shot transfer learning across different datasets (cross-machine), using the knowledge of ASF to diagnose bearings with natural faults (NF). First of all, the model obtains a good feature encoder in the source domain, then defines a fault support set for comparison, and finally adjusts the support set with a very small number of target domain samples to improve the fault diagnosis performance of the model. We carried out experimental verification from many aspects on the ASF and NF datasets provided by Case Western Reserve University (CWRU) and Paderborn University (PU). The results show that the proposed method can fully learn diagnostic knowledge in different ASF datasets and sample numbers, and effectively use this knowledge to accurately identify the health state of the NF bearing, which has strong generalization and robustness. Our method does not need second training, which may be more convenient in some practical applications. Finally, we also discuss the possible limitations of this method.     

一种复杂系统的快速故障诊断方法

为提高复杂系统的故障诊断效率,基于故障诊断树原理,提出并实现了一种快速故障方法。实现该方法主要包括六个关键步骤,分别是故障诊断树的梳理、诊断节点代码的编写录入、Labwindows CVI软件与数据库的通信连接、故障诊断树显示及故障诊断推理、故障诊断节点的编辑、故障结论的存储与显示。在此基础上,设计开发了相应的故障诊断软件,并利用该软件对某型装甲车辆炮控系统进行故障诊断。结果表明,该型软件人机交互性好、操作便捷,基于故障诊断树原理的故障诊断方法可较大的提高故障诊断效率,工程应用前景广阔。     

A Novel Deep Transfer Learning Method for Intelligent Fault Diagnosis Based on Variational Mode Decomposition and Efficient Channel Attention

In recent years, deep learning has been applied to intelligent fault diagnosis and has achieved great success. However, the fault diagnosis method of deep learning assumes that the training dataset and the test dataset are obtained under the same operating conditions. This condition can hardly be met in real application scenarios. Additionally, signal preprocessing technology also has an important influence on intelligent fault diagnosis. How to effectively relate signal preprocessing to a transfer diagnostic model is a challenge. To solve the above problems, we propose a novel deep transfer learning method for intelligent fault diagnosis based on Variational Mode Decomposition (VMD) and Efficient Channel Attention (ECA). In the proposed method, the VMD adaptively matches the optimal center frequency and finite bandwidth of each mode to achieve effective separation of signals. To fuse the mode features more effectively after VMD decomposition, ECA is used to learn channel attention. The experimental results show that the proposed signal preprocessing and feature fusion module can increase the accuracy and generality of the transfer diagnostic model. Moreover, we comprehensively analyze and compare our method with state-of-the-art methods at different noise levels, and the results show that our proposed method has better robustness and generalization performance.     

基于BP神经网络的变速箱故障诊断方法研究

针对变速箱的工作时间不能真实反映实际健康状况的问题,通过提取变速箱的振动信号作为状态参数,建立了基于BP神经网络的变速箱故障诊断模型。该模型首先提取振动信号中对故障反映灵敏的成分作为特征值,获得BP神经网络的训练数据,并通过对比确定最优的隐含层节点数,确定BP神经网络的结构参数。模型训练结束后,以验证数据为例进行故障诊断研究,并对诊断结果进行评估。评估结果表明,该模型准确度高,具有较好的应用和推广价值。     

Research on Twin Extreme Learning Fault Diagnosis Method Based on Multi-Scale Weighted Permutation Entropy

Due to the complicated engineering operation of the check valve in a high−pressure diaphragm pump, its vibration signal tends to show non−stationary and non−linear characteristics. These leads to difficulty extracting fault features and, hence, a low accuracy for fault diagnosis. It is difficult to extract fault features accurately and reliably using the traditional MPE method, and the ELM model has a low accuracy rate in fault classification. Multi−scale weighted permutation entropy (MWPE) is based on extracting multi−scale fault features and arrangement pattern features, and due to the combination of extracting a sequence of amplitude features, fault features are significantly enhanced, which overcomes the deficiency of the single−scale permutation entropy characterizing the complexity of vibration signals. It establishes the check valve fault diagnosis model from the twin extreme learning machine (TELM). The TELM fault diagnosis model established, based on MWPE, aims to find a pair of non−parallel classification hyperplanes in the equipment state space to improve the model’s applicability. Experiments show that the proposed method effectively extracts the characteristics of the vibration signal, and the fault diagnosis model effectively identifies the fault state of the check valve with an accuracy rate of 97.222%.     

基于极化因子神经网络的火电厂制粉系统故障诊断技术

制粉系统是火电厂的主要设备,其安全稳定运行对发电企业的经济生产具有十分重要的意义。针对制粉系统的运行特性和故障分析,提出了基于极化因子神经网络的火电厂制粉系统故障诊断方法,该方法将故障征兆相应的过程变量作为输入,将制粉系统故障类型作为输出,通过训练神经网络建立其系统故障诊断模型,其中训练过程中采用极化因子来自动调整神经网络的收敛速度,从而在满足误差目标的前提下,防止其陷入局部极小。选取实际火电厂制粉系统3个典型故障及其相对应的9个故障征兆参数进行了实验。结果表明,该方法具有良好的收敛性,完全可以满足火电厂制粉系统现场故障诊断的要求。     

基于SVM集成学习的网络故障诊断方法

针对现有SVM多分类方法在网络故障诊断中识别精度较低的问题,本文提出一种基于二叉树结构和模型二重扰动的SVM集成学习算法。通过集成学习思想提高网络故障诊断的精度。在集成过程中对二叉树结构和核参数进行扰动,加大个体分类器的差异度,提升了诊断模型的泛化性。在实际网络中的诊断实验表明所提的方法较二叉树等其它SVM多分类方法具有更高的诊断精度。     

Influence of Operating Parameters on Unbalance in Rotating Machinery Using Response Surface Method

Wide range of rotating machinery contains an inherent amount of unbalance which leads to increase in the vibration level and related faults. In this work, the effect of different operating conditions viz. the unbalanced weight, radius, speed and position of the rotor disc on the unbalance in rotating machine are studied experimentally and analyzed by using Response Surface Methodology (RSM). RSM is a technique which consists of mathematical and statistical methods to develop the relationship between the inputs and outputs of a system by distinct functions. L27 Orthogonal Array (OA) was developed by using Design of Experiments (DOE) according to which experimentation has been carried out. Three accelerometer sensors were mounted to record the vibration responses (accelerations) in radially vertical, horizontal and axial directions. The responses recorded as root mean square values are then analysed using RSM. The relationship between response and operating factors has been established by developing a second order, non-linear mathematical model. Analysis of variance (ANOVA) has been performed for verification of the developed mathematical models. Results obtained from the analysis show that the unbalance weight and speed are most significant operating conditions that contribute the most to the effect the unbalance has on the rotating spindle.     

Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network

Deep learning bearing-fault diagnosis has shown strong vitality in recent years. In industrial practice, the running state of bearings is monitored by collecting data from multiple sensors, for instance, the drive end, the fan end, and the base. Given the complexity of the operating conditions and the limited number of bearing-fault samples, obtaining complementary fault features using the traditional fault-diagnosis method, which uses statistical characteristic in time or frequency, is difficult and relies heavily on prior knowledge. In addition, intelligent bearing-fault diagnosis based on a convolutional neural network (CNN) has several deficiencies, such as single-scale fixed convolutional kernels, excessive dependence on experts’ experience, and a limited capacity for learning a small training dataset. Considering these drawbacks, a novel intelligent bearing-fault-diagnosis method based on signal-to-RGB image mapping (STRIM) and multichannel multiscale CNN (MCMS-CNN) is proposed. First, the signals from three different sensors are converted into RGB images by the STRIM method to achieve feature fusion. To extract RGB image features effectively, the proposed MCMS-CNN is established, which can automatically learn complementary and abundant features at different scales. By increasing the width and decreasing the depth of the network, the overfitting caused by the complex network for a small dataset is eliminated, and the fault classification capability is guaranteed simultaneously. The performance of the method is verified through the Case Western Reserve University’s (CWRU) bearing dataset. Compared with different DL approaches, the proposed approach can effectively realize fault diagnosis and substantially outperform other methods.     

Simultaneously Improve Transferability and Discriminability for Adversarial Domain Adaptation

Although adversarial domain adaptation enhances feature transferability, the feature discriminability will be degraded in the process of adversarial learning. Moreover, most domain adaptation methods only focus on distribution matching in the feature space; however, shifts in the joint distributions of input features and output labels linger in the network, and thus, the transferability is not fully exploited. In this paper, we propose a matrix rank embedding (MRE) method to enhance feature discriminability and transferability simultaneously. MRE restores a low-rank structure for data in the same class and enforces a maximum separation structure for data in different classes. In this manner, the variations within the subspace are reduced, and the separation between the subspaces is increased, resulting in improved discriminability. In addition to statistically aligning the class-conditional distribution in the feature space, MRE forces the data of the same class in different domains to exhibit an approximate low-rank structure, thereby aligning the class-conditional distribution in the label space, resulting in improved transferability. MRE is computationally efficient and can be used as a plug-and-play term for other adversarial domain adaptation networks. Comprehensive experiments demonstrate that MRE can advance state-of-the-art domain adaptation methods.     

一种模拟滤波器电路的故障诊断方法

针对模拟滤波器电路,提出了一种基于测前仿真和测后仿真相结合的故障诊断方法。在测前仿真环节,通过仿真获取电路正常状态及故障状态的幅频响应曲线,引入“区别度”计算电路故障状态和正常状态的区分程度,从而确定电路的可测故障集,并通过频率选择建立可测故障集的故障字典。在测后仿真环节,通过不同频率的激励获得电路故障状态的测试数据,再利用“区别度”计算测试数据与故障字典中各故障特征的区分程度,通过最小“区别度”实现故障检测及故障元件的定位。最后通过一个滤波器电路仿真实例,基于PSpice仿真和Matlab程序计算实现了基于测前仿真的可测故障集确定和故障字典建立,以及基于测后仿真的故障检测和故障元件定位,验证了本文提出方法的实用性。     

融入模糊推理的新型故障树诊断方法

在以预防为主、准确、高效武器装备故障诊断的指导思想下,针对故障树诊断法固有的优缺点属性,本文将产生式规则和模糊理论引入故障树中,设计了故障树的确定性诊断和不确定性推理的故障诊断推理方法,将模糊故障机理以“故障树”的方式进行表达,使故障树诊断从确定性诊断领域扩展到模糊诊断领域,并构建了相应诊断算法流程。通过仿真对比,说明了该故障诊断推理方法的正确性,同时还证明了该方法具备快速性、准确性,以及处理模糊故障机理问题的能力。     

基于多种群混沌遗传神经网络的模拟电路故障诊断

针对标准遗传算法优化BP神经网络收敛慢,易陷入局部最优的问题,提出了改进的多种群协同进化遗传算法,该算法改变了以往的随机初始化方法,采用了附加混沌扰动的tent映射初始化均匀分布的种群,提高了初始解的质量;每个种群采用自适应交叉率和变异率,引入移民算子实现种群间的横向联系;算法通过多种群的协同进化和种群间的个体移植提高了算法的搜索均匀性和效率;仿真实验表明该算法误差小,收敛速度快,诊断正确率高,较好地解决了模拟电路的软故障诊断问题。     

A Machine Learning Approach for Gearbox System Fault Diagnosis

This study proposes a fully automated gearbox fault diagnosis approach that does not require knowledge about the specific gearbox construction and its load. The proposed approach is based on evaluating an adaptive filter’s prediction error. The obtained prediction error’s standard deviation is further processed with a support-vector machine to classify the gearbox’s condition. The proposed method was cross-validated on a public dataset, segmented into 1760 test samples, against two other reference methods. The accuracy achieved by the proposed method was better than the accuracies of the reference methods. The accuracy of the proposed method was on average 9% higher compared to both reference methods for different support vector settings.     

基于多级BP神经网络的无线电罗盘多故障诊断研究

【】本文针对过拟合现象提出了基于提前停止法的学习率可变BP算法,运用多级BP神经网络诊断思想,提出了基于多级BP神经网络的多故障诊断方法。文中根据BP神经网络改进算法和网络撕裂法,对具体的机载无线电罗盘测向电路建模仿真,将复杂的无线电罗盘电路分解为3个子网络,并对每个子网络建立合适的故障集,快速准确诊断得出电路中的故障模块。