基于稀疏自动编码深度神经网络的感应电动机故障诊断

针对目前感应电动机故障诊断大多采用监督学习提取故障特征的现状,提出一种将去噪编码融入稀疏自动编码器的深度神经网络,实现非监督学习的特征提取并用于感应电动机的故障诊断。稀疏自动编码器通过自动学习复杂数据的内在特征来提取简明的数据特征表达。为提高特征表达的鲁棒性,在稀疏编码器的基础上融入去噪编码,提取更有效的特征表达用来训练神经网络分类器进而完成整个深度神经网络的构建,并结合反向传播算法对深度神经网络进行整体微调,提升故障分类的准确度。整个训练过程引入"dropout"训练技巧,减少因过拟合带来的预测误差。试验结果表明,相比传统反向传播(Back propagation,BP)神经网络,提出的深度神经网络能更有效地实现感应电动机故障诊断。     

A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network

Fault diagnosis of rotating machinery is crucial to improve safety, enhance reliability and reduce maintenance cost. The manual feature extraction and selection of traditional fault diagnosis methods depend on signal processing skills and expert experience, which is labor-intensive and time-consuming. As a typical intelligent fault diagnosis method, the convolutional neural network automatically learns features from original data, but it is extremely difficult to design and train a deep network architecture. This paper proposes a fault diagnosis scheme combined of hierarchical symbolic analysis (HSA) and convolutional neural network (CNN), which achieves laborsaving and timesaving preliminary feature extraction and accomplishes automatically feature learning with simplified network architecture. Firstly, hierarchical symbolic analysis is employed to extract features from original signals. The extracted features are able to identify different health conditions under various operating conditions. Then, convolutional neural network instead of human labor is used to learn the complex non-linear relationship between features and health conditions automatically. The architecture of CNN diagnosis model is simple and convenient to implement. Finally, a centrifugal pump dataset and a motor bearing dataset are adopted to validate the effectiveness of the proposed method. The diagnosis results show that the proposed method exhibits superior performance compared with shallow methods and deep learning methods.     

残差卷积自编码网络无监督迁移轴承故障诊断

深度学习类轴承故障智能诊断研究中,一般会假设训练数据与测试数据同分布且典型故障样本充足,而实际工况复杂多变,难以获得大量标签数据。将残差学习引入卷积自编码,并结合迁移学习,提出了基于残差卷积自编码无监督域自适应迁移的故障诊断方法。堆叠一维卷积自编码进行特征提取,通过残差学习避免过拟合,提高学习效率;融合多层多核概率分布适配来约束网络学习域不变特征;实现了基于无监督域自适应迁移学习的故障诊断,并获得了较高准确率的识别结果。采用凯斯西储大学轴承数据集进行验证,结果证明了所提出方法的有效性,此外还对主要参数及其影响进行了探讨并给出了对比结果。     

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

Roller bearing failure is one of the most common faults in rotating machines.Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed.But feature extraction from fault signals requires expert prior information and human labour.Recently,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data.Given its robust per-formance in image recognition,the convolutional neural network(CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions.This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis.The first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration signals.To begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration Images.Once the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration images.Finally,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture features.In the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions.Two cases of fault classification of rolling element bearings are used to validate the proposed method.Experimental results of this investigation demonstrate that RGBVI-CNN can generate advan-tageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy.Moreover,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy,precision,recall,and F-score.     

一种无监督的轴承健康指标及早期故障检测方法

提出了一种无监督的轴承健康指标及早期故障检测方法。设计了一种可以有效提取轴承状态特征的深度可分离卷积自编码器模型,以编码器的输出作为轴承状态特征表示,使用Bray-Curtis距离计算退化状态特征和健康状态特征之间的距离作为轴承状态的健康指标（BC-HI）。基于健康指标BC-HI提出了一种结合Savitzky-Golay滤波的早期故障检测方法,根据健康指标的趋势获取异常阈值,判断早期故障的发生。为验证所提方法的有效性及泛化能力,在轴承加速寿命试验数据集上进行试验,试验结果表明提出的健康指标可以反映轴承的退化趋势,并且对早期故障较为敏感,具有较强的泛化能力,与孤立森林、支持向量机等方法相比,首次故障检测时间更加提前,误报警率更低,具有一定的应用价值。     

基于改进深层极限学习机的故障诊断方法

提出一种新的基于稀疏和近邻保持理论深层极限学习机（sparsity and neighborhood preserving deep extreme learning machines，简称 SNP-DELM））的滚动轴承故障诊断方法。首先，将极限学习机（extreme learning machine，简称ELM）与自编码器（autoencoder，简称AE）相结合，提出一种ELM-AE的结构，利用自编码器对极限学习机的隐含层进行分层；其次，将稀疏与近邻思想融入深层网络中，在投影过程中，通过稀疏表示保持数据的全局结构，通过近邻表示保持数据的局部流形结构，无监督地逐层提取数据的深层特征；最后，通过监督学习求解最小二乘进行分类诊断。将该方法用于风机滚动轴承故障诊断实验，并与ELM、堆叠降噪自编码器（stacked autoencoder，简称SAE）、深层极限学习机（deep extreme learning machine，简称DELM）、卷积神经网络（convolution neural network，简称CNN）等方法进行对比，实验结果表明，SNP-DELM算法相对于现有的几种算法具有更高的准确率和稳定性。     

基于增强深度自编码网络的滚动轴承故障诊断方法

为了提高深度自编码网络的特征挖掘能力,自适应地选取网络超参数,提出了一种增强深度自编码网络,并将其应用于滚动轴承故障诊断。采用最大相关熵代替均方误差作为自编码器的损失函数,加入稀疏惩罚项和嵌入非负约束因子的收缩惩罚项,进一步减小重构误差;通过灰狼优化算法自适应地选取网络关键参数。实验分析结果表明,与现有方法相比,该方法具有更强的特征提取能力与稳定性,对变工况下的轴承振动数据也能达到较高的识别精度。     

A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery

To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application.     

基于DBN的故障特征提取及诊断方法研究

随着装备日趋复杂化,依靠专家经验或信号处理技术人工提取和选择故障特征变得越来越困难。此外,以BP神经网络、SVM为代表的浅层模型难以表征被测信号与装备健康状况之间复杂的映射关系,且面临维数灾难等问题。结合深度置信网络(DBN)在提取特征和处理高维、非线性数据等方面的优势,提出一种基于深度置信网络的故障特征提取及诊断方法。该方法通过深度学习利用原始时域信号训练深度置信网络并完成智能诊断,其优势在于能够摆脱对大量信号处理技术与诊断经验的依赖,完成故障特征的自适应提取与健康状况的智能诊断,该方法对时域信号没有周期性要求,具有较强的通用性和适应性。在仿真数据集和轴承数据集上进行了故障特征提取和诊断实验,实验结果表明:本文提出的方法能够有效地从原始信号中进行多种工况、多种故障位置和多种故障程度的故障特征提取和诊断,并且具有较高的故障识别精度。     

基于深度学习理论的机械装备大数据健康监测方法

机械装备正在朝着高速、高精、高效方向发展,为了确保这些装备的健康运行,健康监测系统采集了海量数据来反映机械的健康状况,促使机械健康监测领域进入了“大数据”时代。机械大数据具有大容量、多样性与高速率的特点,研究和利用先进的理论与方法,从机械装备大数据中挖掘信息,高效、准确地识别装备的健康状况,成为机械装备健康监测领域面临的新问题。深度学习理论作为模式识别和机器学习领域最新的研究成果,以强大的建模和表征能力在图像和语音处理等领域的大数据处理方面取得了丰硕的成果。结合机械大数据的特点与深度学习的优势,提出了一种新的机械装备健康监测方法。该方法通过深度学习利用机械频域信号训练深度神经网络,其优势在于能够摆脱对大量信号处理技术与诊断经验的依赖,完成故障特征的自适应提取与健康状况的智能诊断,因此克服了传统智能诊断方法的两大缺陷：需要掌握大量的信号处理技术结合丰富的工程实践经验来提取故障特征;使用浅层模型难以表征大数据情况下信号与健康状况之间复杂的映射关系。试验结果表明,该方法实现了多种工况、大量样本下多级齿轮传动系统不同故障位置不同故障类型的故障特征自适应提取与健康状况准确识别。     

堆栈稀疏自编码器的风力机锥齿轮故障诊断

针对当前齿轮故障诊断存在着准确性不高、主观性强等问题,提出了一种基于堆栈稀疏自编码器(SSAE)和softmax分类器相结合的齿轮故障诊断方法。首先,运用时域分析以及样本熵方法对风力机锥齿轮振动信号进行特征提取,其次,将提取的特征输入到SSAE中进一步学习目标数据的深层本质特征,并进行特征降维,最后使用softmax分类器中进行分类识别。通过实验结果表明,和文中其他浅层学习模型相比,SSAE能够从齿轮振动信号中有效学习到所需的深层本质特征,拥有更高的识别准确率,因而证实了该方法优越性。     

一种乳化液泵分级故障诊断方法

针对乳化液泵故障机理复杂、故障诊断难的现状,提出一种乳化液泵分级故障诊断方法。首先,通过深度自编码网络(Deep Auto-Encoder Network,DAEN)实现乳化液泵故障的第一级诊断,以乳化液泵的14个特征参数作为输入,自适应特征学习,识别故障形式;然后,通过专家系统实现乳化液泵故障的第二级诊断,将已识别的故障形式与必要故障信息作为专家系统输入,得到明确的故障定位。实验表明,深度自编码网络平均准确率98.712%,优于深度神经网络和卷积神经网络,可靠性高,可以完成第一级诊断任务,然后通过专家系统完成第二级诊断任务,分析产生原因,操作简单。将该方法编制成后台可运行的程序,嵌入煤矿综采工作面智慧云平台。经过实际测试,该故障分级诊断方法能够快速有效定位故障位置,提高诊断精度。     

一种频域特征提取自编码器及其在故障诊断中的应用研究

提出一种可以直接从振动信号中提取频域特征的非对称自编码器方法。与传统自编码器以重构振动信号作为目标输出不同,频域自编码器使用振动信号的频谱作为目标输出,这种非对称的自编码器可以学习振动信号与其频谱之间的映射关系,使得编码器可以输出频域特征。为了说明提出的频域自编码器的特征提取效果,在轴承数据集上进行特征提取和故障诊断实验,在没有引入标签信息的情况下,频域自编码器提取到的特征表现出较好的聚类效果,能够区分轴承的不同故障类型;进一步进行了泛化实验,训练分类器时使用1%的有标签样本,可以达到90%以上的故障分类准确率。实验结果表明,频域自编码器与传统自编码器相比,可以更好地提取振动信号的故障特征信息,具有一定的实用价值。     

基于自动编码器数据降维的滚动轴承故障诊断研究

针对滚动轴承振动信号多域特征数据维数较高的问题,采用自动编码器(Auto-Encoder,AE)对特征数据进行降维处理,实现故障诊断.该方法首先提取滚动轴承振动信号中的特征数据,其次通过自动编码器对特征数据进行降维,最后将降维后的数据用于训练BP(Back Propagation)神经网络,并进行故障诊断.为验证自动编...     

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.     

基于变分自编码器的轴承健康状态评估

针对现有数据驱动型轴承健康状态评估方法普遍存在的特征信息损失大、泛化能力弱和数据依赖强等问题，提出了一种面向高熵特征数据的变分自编码器(variational auto--encoder, 简称VAE)轴承健康状态评估模型。该模型通过学习健康状态下轴承振动信号频谱在特征空间中的高维潜在概率分布，实现对轴承运行健康状态的定量评估。首先，对基于VAE的健康状态评估模型进行理论阐述；其次，建立基于变分证据下界的状态评估指标；最后，通过对比实验证明：变分自编码器在处理轴承运行状态评估方面具有良好的准确度，对异常状态更为敏感；无需人为提取特征和复杂的参数设置，不需对特定的系统进行针对性的参数设置和调校；在小容量训练数据集上仍具备良好的鲁棒性，在工程应用上具有一定的推广价值。     

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Hydraulic systems have the characteristics of strong fault concealment,powerful nonlinear time-varying signals,and a complex vibration transmission mechanism;hence,diagnosis of these systems is a challenge.To provide accurate diagnosis results automatically,numerous studies have been carried out.Among them,signal-based methods are commonly used,which employ signal processing techniques based on the state signal used for extracting features,and further input the features into the classifier for fault recognition.However,their main deficiencies include the following:(1)The features are manually designed and thus may have a lack of objectivity.(2) For signal processing,feature extraction and pattern recognition are conducted using independent models,which cannot be jointly optimized globally.(3) The machine learning algorithms adopted by these methods have a shallow architecture,which limits their capacity to deeply mine the essential features of a fault.As a breakthrough in artificial intelligence,deep learning holds the potential to overcome such deficiencies.Based on deep learning,deep neural networks(DNNs) can automatically learn the complex nonlinear relations implied in a signal,can be globally optimized,and can obtain the high-level features of multi-dimensional data.In this paper,the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed.The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented,and the main ideas,methods,and principles of several typical DNNs are described and summarized.The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed,and research ideas for applying DNNs for hydraulic fault diagnosis are proposed.Meanwhile,the research advantages and development trend of DNNs(both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed.Furthermore,the fault characteristics of a complex hydraulic system are summarized and discussed,and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.     

Fault diagnosis method of rolling bearing based on deep belief network

A method based on the theory of deep learning and feature extraction and a fault diagnosis model of a rolling bearing based on deep belief network are proposed in this study considering the complex, nonlinear, and non-stationary vibration signal of the rolling bearing. To some extent, the method avoids the complex structure of deep neural network and can be easily trained. Experimental results show that the recognition rate of the method reaches 100 %. The method can identify various types of faults accurately and has good fault diagnosis capability, which can provide the convenience for maintenance.     

一种面向旋转机械的基于 Transformer 特征提取的域自适应故障诊断

针对基于深度学习的旋转机械故障诊断方法在新工作条件下缺乏标注数据、跨域诊断精度较低的问题,提出了一种基 于 Transformer 的域自适应故障诊断方法。 采用 Transformer 的变体 VOLO 构造特征提取器以获取细粒度更佳的故障特征表示。 利用源域数据进行监督学习对源域和目标域数据的特征提取器进行预训练,并且冻结源域提取器参数以获取固定的源域特征。 利用域对抗自适应策略和局部最大平均差异结合目标域未标注数据训练目标域特征提取器,实现源域特征与目标域特征的边 缘分布、条件分布对齐。 通过两个多工况实验对所提出的故障诊断算法进行了验证,结果表明提出的基于 Transformer 特征提 取的域自适应故障诊断方法相比 5 种传统域自适应方法,在齿轮和轴承数据集上分别平均提升了 22. 15% 和 11. 67% 的诊断精 度,证明所提出方法对于跨域诊断精度具有提升作用。     

基于深度学习特征提取和粒子群支持向量机状态识别的齿轮智能故障诊断

针对齿轮故障诊断问题,利用数理统计特征提取方法、深度学习神经网络、粒子群算法和支持向量机等技术,提出了一种基于深度学习特征提取和粒子群支持向量机状态识别相结合的智能诊断模型。该模型利用深度学习自适应提取的频谱特征与数理统计方法提取的时域特征相结合组成联合特征向量,然后利用粒子群支持向量机对联合特征向量进行故障诊断。该模型在对多级齿轮传动系统试验台的故障诊断中实现了中速轴大齿轮不同故障类型的可靠识别,获得了满意的诊断结果。应用结果也验证了基于深度学习自适应提取频谱特征的有效性。