基于二次数据增强和深度卷积的滚动轴承故障诊断研究

滚动轴承是旋转机械的重要组成部件之一,及时准确地故障诊断在现代工业系统的可靠性和安全性中起着重要作用。然而现有故障诊断方法多是面向平衡数据集进行研究。针对实际工况下,正常样本丰富、故障样本少的类别失衡情形所导致的轴承故障诊断能力和泛化能力较差等问题,提出一种基于二次数据增强和深度卷积的故障诊断模型。该方法首先构造不同的数据集,研究类别不平衡情形对故障诊断性能的影响;其次,基于重采样方法将数据集重构为平衡数据集,并对其进行二次数据增强,提高样本点的利用率;然后,利用改进的深度一维卷积网络提取信号特征,对滚动轴承故障信息进行充分表征;最后结合集成学习投票分类思想进行故障分类与诊断。试验通过t-SNE及多种指标进行评估,同时与其他方法进行对比,结果表明,所提模型具有更高的诊断精度与诊断速度,鲁棒性与通用性较好,能够很好地适用于类不平衡情形下的滚动轴承故障诊断。     

面向变工况下工业流数据故障诊断的持续迁移学习系统

机器学习模型在智能故障诊断中取得了显著成功,但主要应用于静态场景。 在实际场景中,新的故障类别数据以流形 式不断产生,且数据分布随机械设备运行条件变化而发生变化,导致连续流数据具有非独立同分布的特征,这种面向非独立同 分布连续流数据的诊断问题被称为持续迁移诊断问题。 针对此问题,本文提出了一种基于持续迁移学习系统(CTLS)的故障诊 断方法。 该方法设计了域适应学习损失函数和持续迁移学习机制,能有效处理变工况下的工业流数据,无需重放旧类别数据便 能够能学习新类别知识。 此外,利用机械故障诊断案例评估该方法的性能,分析结果证明 CTLS 能够高效处理变工况条件下的 工业流数据,是一种极具潜力的解决实际工业问题的可靠工具。     

大数据下机械智能故障诊断的机遇与挑战

机械故障是风力发电设备、航空发动机、高档数控机床等大型机械装备安全可靠运行的"潜在杀手"。故障诊断是保障机械装备安全运行的"杀手锏"。由于诊断的装备量大面广、每台装备测点多、数据采样频率高、装备服役历时长,所以获取了海量的诊断数据,推动故障诊断领域进入了"大数据"时代。而机械智能故障诊断有望成为大数据下诊断机械装备故障的"一把利器"。与此同时,大数据给机械智能故障诊断的深入研究和应用提供了新的机遇:"数据为王"的学术思想有望成为主流、诊断整机或系统级对象成为可能、全面解析故障演化过程成为趋势等;但也遇到了新的挑战:数据大而不全呈"碎片化"、故障特征提取受制于人为经验、浅层诊断模型诊断精度低等。阐述了机械智能故障诊断大数据的特点;从信号获取、特征提取、故障识别与预测三个环节,综述了机械智能故障诊断的国内外研究进展和发展动态;指出了机械智能故障诊断理论与方法在大数据背景下的挑战;最后讨论了应对这些挑战的解决途径与发展趋势。     

不平衡技术在轴承故障诊断中的应用

为解决实际工程环境中因轴承故障数据缺失引起的数据不平衡,进而影响模型诊断的问题,提出了不平衡数据处理技术在轴承故障诊断中的应用,即使用少量数据,训练出一个能够诊断多种轴承故障的模型.针对不同种类故障数据的边界混淆及类内不平衡,首先对少数类样本进行高斯混合模型( GMM )聚类,根据簇密度分布函数使用 GMM-SMOTE 进行权重采样,然后针对边界混淆问题使用 Tomek’s Link 数据清洗技术进行边界混淆样本数据清洗,形成类内类间平衡的数据集,结合超参数优化的支持向量机(SVM )模型进行诊断分析.实验结果表明,该方法效果良好,在轴承的故障诊断中有较好的实际工程意义和推广性.     

半监督阶梯网络和GAN在滚动轴承故障诊断的应用

滚动轴承是旋转机械的关键部件，统计表明，约30%的旋转机械故障出现在滚动轴承处。人工智能的发展给工业生产中的故障诊断提供了新途径，深度学习已经成为旋转机械故障诊断的一种新颖且有效的方法。普通神经网络的训练需要大量各种情况下的带标签数据，然而现实中工业数据存在不平衡和标签成本高等问题，限制深度学习在工业实际中故障诊断的发展。这里针对滚动轴承监测数据不平衡和缺少标签等问题影响深度学习在工业领域落地应用的情况，提出一种基于半监督阶梯网络和生成对抗网络（GAN）的方法，建立GAN-SSLadder分类模型，基于公开的轴承故障数据库，实现在标签少，数据量小的情况下测试集的识别准确率达到95.5%。     

风力发电机组故障诊断与预测技术研究综述

随着风力发电机组装机容量的快速发展,累计运行时间的持续增长,风电机组的维护问题日益突出,迫切需要研发有效的风电机组故障诊断与预测系统。从故障诊断和故障预测两个方面,归纳风力发电机组的主要故障特点;针对故障诊断难点问题,分析和总结基于振动、电气信号分析和模式识别算法的故障诊断方法的研究现状,指出各种方法的技术特点、局限性和今后的发展趋势;针对风电机组中机械结构和电子系统性能退化的各自特点,归纳当前的研究进展,提出物理失效模型和数据驱动模型融合的故障预测方法;最后,归纳了利用风力发电机组数据采集与监控系统(SCADA)数据进行故障诊断与预测的最新进展及需要进一步研究的问题。     

基于偏置经验特征映射的电路故障诊断方法

基于支持向量机的模拟电路故障诊断方法已经成为故障诊断领域的研究热点.然而,在该方法实用化过程中,故障样本集中存在的不平衡分布问题严重影响了该方法的整体诊断性能.针对该问题,提出一种基于偏置经验特征映射的故障诊断方法,该方法将故障样本集映射至经验特征空间,并在该特征空间中使用偏置判别分析准则作为核函数优化的目标函数,最大化所有正常样本同故障样本中心的距离,从而提高故障诊断方法的整体诊断能力.标准数据集以及真实电路上的实验效果表明,提出的方法可以大大缓解由于样本不平衡造成的支持向量机诊断效果下降的问题,从而提高了基于支持向量机的电路故障诊断法方法的适用范围.     

多传感器信息融合技术在液压系统故障诊断中的应用

针对单参数诊断复杂系统中出现的信息不完整和不确定性问题,提出基于BP神经网络和D-S证据理论的多传感器信息融合故障诊断方法。为了简化BP神经网络结构,首先利用两并行BP神经网络对故障数据进行诊断;之后,D-S通过证据理论融合局部诊断结果,实现对于不准确信息的准确判断,获得准确诊断结果。该方法适用于特定类型火箭发射器液压驱动伺服系统(HDSS)的故障诊断,实现了对于液压伺服驱动系统中主要部件的故障定位和诊断,有效提高了系统可靠性。     

净化装置风机故障诊断的神经网络方法

说明了神经网络应用于故障诊断的基本原理。应用该方法，对某风机振动故障进行了诊断，诊断结果为不平衡和不对中，与实际情况吻合，这表明神经网络应用风机故障诊断完全可行。     

基于数据驱动的电动执行器故障诊断方法

本文针对在流程工业自动化系统中大量使用的电动执行器,结合热工系统的应用背景,提出了一种基于数据驱动的电动执行器故障诊断方法.该故障诊断方法包括特征提取和故障识别两部分:采用主成分分析法提取故障特征信号;采用基于支持向量机的方法进行故障识别.构造多类支持向量机分类器,建立了电动执行器多故障诊断模型.仿真实验研究证明了该诊断方法的可行性和有效性.     

Application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine: A case study

This paper presents a case study of the application of a data-driven monitoring technique to diagnose air leaks in an automotive diesel engine. Using measurement signals taken from the sensors/actuators which are present in a modern automotive vehicle, a data-driven diagnostic model is built for condition monitoring purposes. Detailed investigations have shown that measured signals taken from the experimental test-bed often contain redundant information and noise due to the nature of the process. In order to deliver a clear interpretation of these measured signals, they therefore need to undergo a ‘compression’ and an ‘extraction’ stage in the modelling process. It is at this stage that the proposed data-driven monitoring technique plays a significant role by taking only the important information of the original measured signals for fault diagnosis purposes. The status of the engine's performance is then monitored using this diagnostic model. This condition monitoring process involves two separate stages of fault detection and root-cause diagnosis.The effectiveness of this diagnostic model was validated using an experimental automotive 1.9 L four-cylinder diesel engine embedded in a chassis dynamometer in an engine test-bed. Two joint diagnostics plots were used to provide an accurate and sensitive fault detection process. Using the proposed model, small air leaks in the inlet manifold plenum chamber with a diameter size of 2–6 mm were accurately detected. Further analyses using contribution to T² and Q statistics show the effect of these air leaks on fuel consumption. It was later discovered that these air leaks may contribute to emissions fault.In comparison to the existing model-based approaches, the proposed method has several benefits: (i) it makes no simplifying assumptions, as the model is built entirely from the measured signals; (ii) it is simple and straight-forward; (iii) there is no additional hardware required for modelling; (iv) it is a time and cost-efficient way to deliver condition monitoring (i.e. fault diagnosis application); (v) it is capable of pin-pointing the root-cause and the effect of the problem; and (vi) it is feasible to be implemented in practice.     

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.     

Basic research on machinery fault diagnostics: Past,present, and future trends

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.     

保证数据隐私的装备协同智能故障诊断联邦迁移学习方法

大数据驱动的机械装备智能故障诊断方法在近年来取得了显著的成果,当前良好的诊断效果主要依赖于大量有标注的状态监测数据以中心化的方式训练模型,然而在实际工程问题中,单一用户往往难以收集充足的高质量训练数据,因此智能诊断方法的实际应用仍存在巨大困难。在工业界,多个用户往往拥有相似的机械装备与各自收集的监测数据,因此联合多用户协同进行故障诊断建模能够良好解决数据稀缺问题。然而,数据隐私性至关重要,不同用户往往不愿将私有数据与其他用户共享,给协同建模带来巨大挑战。提出保证数据隐私的装备协同智能故障诊断方法 FedTL,各用户私有数据不出本地完成模型训练,多用户间传输共享数据高级表征;提出软标签信息传输方法,通过捕捉共享数据不同故障模式关系实现对私有数据诊断知识的传递;考虑多用户装备工况不同等场景,提出联邦迁移学习方法。通过轴承状态监测试验对所提方法进行验证,结果表明所提方法能够保证数据隐私良好完成多用户协同智能故障诊断。     

Mass spectrometry-based "omics" technologies in cancer diagnostics

Many "omics" techniques have been developed for one goal: biomarker discovery and early diagnosis of human cancers. A comprehensive review of mass spectrometry-based "omics" approaches performed on various biological samples for molecular diagnosis of human cancers is presented in this article. Furthermore, the existing and potential problems/solutions (both de facto experimental and bioinformatic challenges), and future prospects have been extensively discussed. Although the use of present omic methods as diagnostic tools are still in their infant stage and consequently not ready for immediate clinical use, it can be envisaged that the "omics"-based cancer diagnostics will gradually enter into the clinic in next 10 years as an important supplement to current clinical diagnostics.     

Deep residual learning-based fault diagnosis method for rotating machinery

Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.     

基于改进DQN网络的滚动轴承故障诊断方法

针对实际中滚动轴承正常和故障状态下的振动数据不平衡,且故障诊断准确率不高的问题,基于深度强化学习,提出一种改进深度Q网络(DQN)的滚动轴承故障诊断方法.该方法将振动信号进行短时傅里叶变换,构建时频图样本集;提出把K-means算法中样本到中心点的距离作为回报值的偏置,以不平衡比为基准,为训练集构建具有个性化的回报函数...     

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Supervised fault diagnosis typically assumes that all the types of machinery failures are known.However,in practice unknown types of defect,i.e.,novelties,may occur,whose detection is a challenging task.In this paper,a novel fault diagnostic method is developed for both diagnostics and detection of novelties.To this end,a sparse autoencoder-based multi-head Deep Neural Network(DNN)is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data.The detection of novelties is based on the reconstruction error.Moreover,the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function,instead of performing the pre-training and fine-tuning phases required for classical DNNs.The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer.The results show that its performance is satisfactory both in detection of novelties and fault diagnosis,outperforming other state-of-the-art methods.This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect,but also detect unknown types of defects.     

新能源汽车维修中电子诊断技术的应用探讨

随着我国汽车制造工艺逐步完善,汽车制造行业开始重点研发新能源汽车,电子诊断技术使维修工作逐步升级,进一步推动了新能源汽车行业的发展.但是,新能源汽车结构精密,传统汽车故障诊断技术适用性较差,存在检测手段单一、结果准确率低的技术缺陷.本文对电子诊断技术在新能源汽车维修领域中的应用现状开展探讨,对技术未来发展进行探索,希望...     

深度学习在设备故障预测与健康管理中的应用

在智能制造背景下,大数据驱动的设备故障预测与健康管理日益受到各界重视。深度学习能够在层次结构的特征提取过程中发现更多的隐藏知识,在领域自适应方面具有良好的数据适应性,近年来逐渐成为设备故障预测与健康管理的研究热点,并在设备故障诊断和预测中得到了广泛的应用。通过系统回顾近年来深度学习在设备故障预测与健康管理中应用,总结、分类和解释关于这一热点主题的主要文献,讨论了各种体系结构和相关理论。在此基础上,阐述了深度学习在设备故障诊断和预测方面所取得的主要成果、面临的挑战、以及未来的发展趋势,为设备故障预测与健康管理领域选择、设计或实现深度学习架构,提供明确的方向。