大多数风力涡轮机受到24/7的远程监测,以允许早期发现操作问题并产生损坏。我们提出了一种新的故障检测方法,用于不需要任何功能工程的振动监控传动系统。我们的方法依赖于简单的模型体系结构来实践中实现直接实现。我们建议将卷积自动编码器以自动方式从半频谱中识别和提取最相关的功能,从而节省时间和精力。因此,从过去的测量值中学习了受监测组件的正常振动响应的光谱模型。我们证明该模型可以成功区分受损部件,并从其振动响应中检测出受损的发电机轴承和损坏的变速箱零件。使用商用风力涡轮机和测试钻机的测量结果,我们表明,可以在没有光谱特征的常规前期定义的情况下进行风力涡轮机传动系统中的基于振动的故障检测。提出方法的另一个优点是,监测整个半频谱,而不是通常关注各个频率和谐波。
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Terabytes of data are collected every day by wind turbine manufacturers from their fleets. The data contain valuable real-time information for turbine health diagnostics and performance monitoring, for predicting rare failures and the remaining service life of critical parts. And yet, this wealth of data from wind turbine fleets remains inaccessible to operators, utility companies, and researchers as manufacturing companies prefer the privacy of their fleets' turbine data for business strategic reasons. The lack of data access impedes the exploitation of opportunities, such as improving data-driven turbine operation and maintenance strategies and reducing downtimes. We present a distributed federated machine learning approach that leaves the data on the wind turbines to preserve the data privacy, as desired by manufacturers, while still enabling fleet-wide learning on those local data. We demonstrate in a case study that wind turbines which are scarce in representative training data benefit from more accurate fault detection models with federated learning, while no turbine experiences a loss in model performance by participating in the federated learning process. When comparing conventional and federated training processes, the average model training time rises significantly by a factor of 7 in the federated training due to increased communication and overhead operations. Thus, model training times might constitute an impediment that needs to be further explored and alleviated in federated learning applications, especially for large wind turbine fleets.
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现代高性能计算(HPC)系统的复杂性日益增加,需要引入自动化和数据驱动的方法,以支持系统管理员为增加系统可用性的努力。异常检测是改善可用性不可或缺的一部分,因为它减轻了系统管理员的负担,并减少了异常和解决方案之间的时间。但是,对当前的最新检测方法进行了监督和半监督,因此它们需要具有异常的人体标签数据集 - 在生产HPC系统中收集通常是不切实际的。基于聚类的无监督异常检测方法,旨在减轻准确的异常数据的需求,到目前为止的性能差。在这项工作中,我们通过提出RUAD来克服这些局限性,RUAD是一种新型的无监督异常检测模型。 Ruad比当前的半监督和无监督的SOA方法取得了更好的结果。这是通过考虑数据中的时间依赖性以及在模型体系结构中包括长短期限内存单元的实现。提出的方法是根据tier-0系统(带有980个节点的Cineca的Marconi100的完整历史)评估的。 RUAD在半监督训练中达到曲线(AUC)下的区域(AUC)为0.763,在无监督的训练中达到了0.767的AUC,这改进了SOA方法,在半监督训练中达到0.747的AUC,无需训练的AUC和0.734的AUC在无处不在的AUC中提高了AUC。训练。它还大大优于基于聚类的当前SOA无监督的异常检测方法,其AUC为0.548。
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The widespread use of information and communication technology (ICT) over the course of the last decades has been a primary catalyst behind the digitalization of power systems. Meanwhile, as the utilization rate of the Internet of Things (IoT) continues to rise along with recent advancements in ICT, the need for secure and computationally efficient monitoring of critical infrastructures like the electrical grid and the agents that participate in it is growing. A cyber-physical system, such as the electrical grid, may experience anomalies for a number of different reasons. These may include physical defects, mistakes in measurement and communication, cyberattacks, and other similar occurrences. The goal of this study is to emphasize what the most common incidents are with power systems and to give an overview and classification of the most common ways to find problems, starting with the consumer/prosumer end working up to the primary power producers. In addition, this article aimed to discuss the methods and techniques, such as artificial intelligence (AI) that are used to identify anomalies in the power systems and markets.
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燃气轮机发动机是复杂的机器,通常产生大量数据,并且需要仔细监控,以允许具有成本效益的预防性维护。在航空航天应用中,将所有测量数据返回到地面是昂贵的,通常会导致有用,高值,要丢弃的数据。因此,在实时检测,优先级和返回有用数据的能力是至关重要的。本文提出了由卷积神经网络常态模型描述的系统输出测量,实时优先考虑预防性维护决策者。由于燃气轮机发动机时变行为的复杂性,导出精确的物理模型难以困难,并且通常导致预测精度低的模型和与实时执行不相容。数据驱动的建模是一种理想的替代方案,生产高精度,资产特定模型,而无需从第一原理推导。我们提出了一种用于在线检测和异常数据的优先级的数据驱动系统。通过集成到深神经预测模型中的不确定管理,避免了偏离新的操作条件的数据评估。测试是对实际和合成数据进行的,显示对真实和合成故障的敏感性。该系统能够在低功耗嵌入式硬件上实时运行,目前正在部署Rolls-Royce Pearl 15发动机飞行试验。
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Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
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铁路是一个复杂的系统,包括多个基础设施和滚动库存资产。为了安全,可靠,有效地操作系统,需要监视许多组件的条件。为了自动化此过程,可以使用数据驱动的故障检测和诊断模型。但是,实际上,如果培训数据集并不代表所有可能的未来条件,则数据驱动模型的性能可能会受到损害。我们建议通过学习特征表示,一方面是对操作或环境因素不变的,但另一方面,对资产的健康状况的变化敏感。我们评估了如何在有监督的和无监督的故障检测和诊断任务上使用对比度学习,并在铁路系统中进行实际状态监控数据集 - 来自基础架构资产的一个图像数据集和来自滚动库存资产的一次时间序列数据集。首先,我们评估了标有标记图像数据集的铁路卧铺缺陷分类任务上有监督的对比功能学习的性能。其次,我们评估了无监督的对比功能学习的性能,而没有在铁路轮数据集的异常检测任务上访问故障样本。在这里,我们检验了特征编码器对降解的敏感性是否对数据中的新故障模式敏感的假设。我们的结果表明,与最先进的方法相比,对比功能学习可以提高有关卧铺的监督分类任务的绩效。此外,在有关铁路轮的异常检测任务上,与最新方法相比,炮击缺陷的检测得到了改善。
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A new Lossy Causal Temporal Convolutional Neural Network Autoencoder for anomaly detection is proposed in this work. Our framework uses a rate-distortion loss and an entropy bottleneck to learn a compressed latent representation for the task. The main idea of using a rate-distortion loss is to introduce representation flexibility that ignores or becomes robust to unlikely events with distinctive patterns, such as anomalies. These anomalies manifest as unique distortion features that can be accurately detected in testing conditions. This new architecture allows us to train a fully unsupervised model that has high accuracy in detecting anomalies from a distortion score despite being trained with some portion of unlabelled anomalous data. This setting is in stark contrast to many of the state-of-the-art unsupervised methodologies that require the model to be only trained on "normal data". We argue that this partially violates the concept of unsupervised training for anomaly detection as the model uses an informed decision that selects what is normal from abnormal for training. Additionally, there is evidence to suggest it also effects the models ability at generalisation. We demonstrate that models that succeed in the paradigm where they are only trained on normal data fail to be robust when anomalous data is injected into the training. In contrast, our compression-based approach converges to a robust representation that tolerates some anomalous distortion. The robust representation achieved by a model using a rate-distortion loss can be used in a more realistic unsupervised anomaly detection scheme.
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在智能交通系统中,交通拥堵异常检测至关重要。运输机构的目标有两个方面:监视感兴趣领域的一般交通状况,并在异常拥堵状态下定位道路细分市场。建模拥塞模式可以实现这些目标,以实现全市道路的目标,相当于学习多元时间序列(MTS)的分布。但是,现有作品要么不可伸缩,要么无法同时捕获MTS中的空间信息。为此,我们提出了一个由数据驱动的生成方法组成的原则性和全面的框架,该方法可以执行可拖动的密度估计来检测流量异常。我们的方法在特征空间中的第一群段段,然后使用条件归一化流以在无监督的设置下在群集级别识别异常的时间快照。然后,我们通过在异常群集上使用内核密度估计器来识别段级别的异常。关于合成数据集的广泛实验表明,我们的方法在召回和F1得分方面显着优于几种最新的拥塞异常检测和诊断方法。我们还使用生成模型来采样标记的数据,该数据可以在有监督的环境中训练分类器,从而减轻缺乏在稀疏设置中进行异常检测的标记数据。
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目标。植物疾病的可持续管理是具有相关经济和环境影响的开放挑战。最佳策略依赖于有利条件下的现场侦察的人类专业知识,以评估当前存在和程度的疾病症状。这种劳动密集型任务被侦察的大场区域复杂化,与毫米级大小相结合待检测的早期症状。鉴于此,基于图像的早期疾病症状的检测是一种有吸引力的方法来实现这一过程,实现了可持续成本的潜在高吞吐量监测。方法。深入学习已成功应用于各个域,以通过培训程序学习过滤器来获取相关图像特征的自动选择。深入学习最近进入了植物疾病检测领域:在这项工作之后,我们提出了一种深入的学习方法,可以在黄瓜叶上识别白粉病。我们专注于应用于多光谱成像数据的无监督的深度学习技术,我们提出了使用AutoEncoder架构来调查疾病检测的两种策略:i)压缩空间中的特征集群化; II)异常检测。结果。通过定量指数评估了两种提出的方​​法。集群化方法本身并不完全能够提供准确的预测,但它确实迎合了相关信息。异常检测代替分辨率的显着潜力,这可以进一步被开发为具有非常有限数量的标记样本的监督架构之前。
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A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.
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As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy risks. Anomaly-based \acp{IDS} play an essential role in network security. In this paper, we present a practical unsupervised anomaly-based deep learning detection system called ARCADE (Adversarially Regularized Convolutional Autoencoder for unsupervised network anomaly DEtection). With a convolutional \ac{AE}, ARCADE automatically builds a profile of the normal traffic using a subset of raw bytes of a few initial packets of network flows so that potential network anomalies and intrusions can be efficiently detected before they cause more damage to the network. ARCADE is trained exclusively on normal traffic. An adversarial training strategy is proposed to regularize and decrease the \ac{AE}'s capabilities to reconstruct network flows that are out-of-the-normal distribution, thereby improving its anomaly detection capabilities. The proposed approach is more effective than state-of-the-art deep learning approaches for network anomaly detection. Even when examining only two initial packets of a network flow, ARCADE can effectively detect malware infection and network attacks. ARCADE presents 20 times fewer parameters than baselines, achieving significantly faster detection speed and reaction time.
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TimeSeries Partitioning是大多数机器学习驱动的传感器的IOT应用程序的重要步骤。本文介绍了一种采样效率,鲁棒,时序分割模型和算法。我们表明,通过基于最大平均差异(MMD)的分割目标来学习特定于分割目标的表示,我们的算法可以鲁布布地检测不同应用程序的时间序列事件。我们的损耗功能允许我们推断是否从相同的分布(空假设)中绘制了连续的样本序列,并确定拒绝零假设的对之间的变化点(即,来自不同的分布)。我们展示了其在基于环境传感的活动识别的实际IOT部署中的适用性。此外,虽然文献中存在许多关于变更点检测的作品,但我们的模型明显更简单,匹配或优于最先进的方法。我们可以平均地在9-93秒内完全培训我们的模型,而在不同应用程序上的数据的差异很小。
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Inspired by the recent success of deep learning in multiscale information encoding, we introduce a variational autoencoder (VAE) based semi-supervised method for detection of faulty traffic data, which is cast as a classification problem. Continuous wavelet transform (CWT) is applied to the time series of traffic volume data to obtain rich features embodied in time-frequency representation, followed by a twin of VAE models to separately encode normal data and faulty data. The resulting multiscale dual encodings are concatenated and fed to an attention-based classifier, consisting of a self-attention module and a multilayer perceptron. For comparison, the proposed architecture is evaluated against five different encoding schemes, including (1) VAE with only normal data encoding, (2) VAE with only faulty data encoding, (3) VAE with both normal and faulty data encodings, but without attention module in the classifier, (4) siamese encoding, and (5) cross-vision transformer (CViT) encoding. The first four encoding schemes adopted the same convolutional neural network (CNN) architecture while the fifth encoding scheme follows the transformer architecture of CViT. Our experiments show that the proposed architecture with the dual encoding scheme, coupled with attention module, outperforms other encoding schemes and results in classification accuracy of 96.4%, precision of 95.5%, and recall of 97.7%.
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我们描述了作为黑暗机器倡议和LES Houches 2019年物理学研讨会进行的数据挑战的结果。挑战的目标是使用无监督机器学习算法检测LHC新物理学的信号。首先,我们提出了如何实现异常分数以在LHC搜索中定义独立于模型的信号区域。我们定义并描述了一个大型基准数据集,由> 10亿美元的Muton-Proton碰撞,其中包含> 10亿美元的模拟LHC事件组成。然后,我们在数据挑战的背景下审查了各种异常检测和密度估计算法,我们在一组现实分析环境中测量了它们的性能。我们绘制了一些有用的结论,可以帮助开发无监督的新物理搜索在LHC的第三次运行期间,并为我们的基准数据集提供用于HTTPS://www.phenomldata.org的未来研究。重现分析的代码在https://github.com/bostdiek/darkmachines-unsupervisedChallenge提供。
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使用深神经网络算法分析振动数据是检测早期旋转机械损害的有效方法。但是,这些方法的黑框方法通常无法提供令人满意的解决方案,因为人类无法理解分类的原因。因此,这项工作调查了可解释的AI(XAI)算法在基于振动状态监测的卷积神经网络中的应用。为此,将各种XAI算法应用于基于傅立叶变换以及振动信号的顺序分析的分类。将结果可视化,是每分钟旋转(rpm)的函数,频率-RPM映射和订单RPM映射的形状。这允许评估取决于旋转速度和恒定频率的功能的显着性。为了比较XAI方法的解释能力,首先使用具有已知类别特异性特征的合成数据集进行了研究。然后,使用了针对电动机上基于振动的不平衡分类的现实世界数据集,该数据集以广泛的旋转速度运行。特别重点放在数据的可变周期性的一致性上,这转化为现实世界机器的不同旋转速度。这项工作旨在显示此用例的方法的不同优势和劣势:Gradcam,LRP和Lime具有新的扰动策略。
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我们考虑为移动机器人构建视觉异常检测系统的问题。标准异常检测模型是使用仅由非异常数据组成的大型数据集训练的。但是,在机器人技术应用中,通常可以使用(可能很少)的异常示例。我们解决了利用这些数据以通过与Real-NVP损失共同使辅助外离群损失损失共同使实际NVP异常检测模型的性能提高性能的问题。我们在新的数据集(作为补充材料)上进行定量实验,该数据集在室内巡逻方案中设计为异常检测。在不连接测试集中,我们的方法优于替代方案,并表明即使少数异常框架也可以实现重大的性能改进。
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信号处理是几乎任何传感器系统的基本组件,具有不同科学学科的广泛应用。时间序列数据,图像和视频序列包括可以增强和分析信息提取和量化的代表性形式的信号。人工智能和机器学习的最近进步正在转向智能,数据驱动,信号处理的研究。该路线图呈现了最先进的方法和应用程序的关键概述,旨在突出未来的挑战和对下一代测量系统的研究机会。它涵盖了广泛的主题,从基础到工业研究,以简明的主题部分组织,反映了每个研究领域的当前和未来发展的趋势和影响。此外,它为研究人员和资助机构提供了识别新前景的指导。
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Aiot技术的最新进展导致利用机器学习算法来检测网络物理系统(CPS)的操作失败的越来越受欢迎。在其基本形式中,异常检测模块从物理工厂监控传感器测量和致动器状态,并检测这些测量中的异常以识别异常操作状态。然而,由于该模型必须在存在高度复杂的系统动态和未知量的传感器噪声的情况下准确地检测异常,构建有效的异常检测模型是挑战性的。在这项工作中,我们提出了一种新的时序序列异常检测方法,称为神经系统识别和贝叶斯滤波(NSIBF),其中特制的神经网络架构被构成系统识别,即捕获动态状态空间中CP的动态模型;然后,通过跟踪系统的隐藏状态的不确定性随着时间的推移,自然地施加贝叶斯滤波算法的顶部。我们提供定性的和定量实验,并在合成和三个现实世界CPS数据集上具有所提出的方法,表明NSIBF对最先进的方法比较了对CPS中异常检测的最新方法。
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从限制黑暗部门的暗物质颗粒的生产可能导致许多新颖的实验签名。根据理论的细节,质子 - 质子碰撞中的黑暗夸克生产可能导致颗粒的半衰期:黑暗强度的准直喷雾,其中颗粒碰撞器实验只有一些。实验签名的特征在于,具有与喷射器的可见部件相结合的重建缺失的动量。这种复杂的拓扑对检测器效率低下和错误重建敏感,从而产生人为缺失的势头。通过这项工作,我们提出了一种信号不可知的策略来拒绝普通喷射,并通过异常检测技术鉴定半衰期喷射。具有喷射子结构变量的深度神经自动化器网络作为输入,证明了对分析异常喷射的非常有用。该研究重点介绍了半意射流签名;然而,该技术可以适用于任何新的物理模型,该模型预测来自非SM粒子的喷射器的签名。
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