The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery, therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this paper, we present novel weakly-supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called Cluster Centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly-supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyper-parameters related to feature extraction and network architecture is investigated. We find that the proposed SAE based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyper-parameter settings on all performance measures.
Weakly-supervised anomaly detection for multimodal data distributions
School of Marine Science and Technology（海洋科学与技术学院）；Northwestern Polytechnical University（西北工业大学）；School of Computing（计算机学院）；University of Eastern Finland（东芬兰大学）；University of Turku（图尔库大学）；Singapore Institute of Technology（新加坡理工学院）
Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
Department of Electronic Systems, Norwegian University of Science and Technology（电子系统系，挪威科学与技术大学）；Department of Engineering Cybernetics, Norwegian University of Science and Technology（工程控制系，挪威科学与技术大学）；Department of Gas Technology, SINTEF Energy Research（气体技术系，SINTEF能源研究）
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；CreateLink Technology（创联科技）
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Sichuan University（四川大学）；University of Connecticut（康涅狄格大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Adelaide（阿德莱德大学）
Sichuan University（四川大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Adelaide（阿德莱德大学）
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitting different distributions. However, due to the limited number of annotated anomaly samples, directly training networks with the discriminative loss may not be sufficient. To overcome this issue, this paper proposes a novel strategy to transform the input data into a more meaning…
Shanghai University of Finance and Economics（上海财经大学）；University of Science and Technology of China（中国科学技术大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Carnegie Mellon University（卡内基梅隆大学）
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation. To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods. In this study, we present the first comprehensive surve…
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
School of Software and Microelectronics Peking University（北京大学软件与微电子学院）；School of Computer Science Peking University（北京大学计算机学院）；National Engineering Center of Software Engineering Peking University（北京大学软件工程国家工程研究中心）
Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain, such approaches have seen huge applicability in recent years. In this context, Isolation Forest is a popular algorithm able to define an anomaly score by means of an ensemble of peculiar trees called isolation trees. These are built using a random partitioning procedure that is extremely fast and cheap to train. However, we find that the standard algorithm might be improved in terms of memory requirements, latency …
Weakly-Supervised Anomaly Detection in the Milky Way
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Rutgers University（罗格斯大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Bosch Research North America（博世北美研究院）
TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches
Département de Physique Nucléaire et Corpusculaire, University of Geneva（核物理与粒子物理系，日内瓦大学）；Department of Computer Science, University of Geneva（计算机科学系，日内瓦大学）
Humans can easily detect a defect (anomaly) because it is different or salient when compared to the surface it resides on. Today, manual human visual inspection is still the norm because it is difficult to automate anomaly detection. Neural networks are a useful tool that can teach a machine to find defects. However, they require a lot of training examples to learn what a defect is and it is tedious and expensive to get these samples. We tackle the problem of teaching a network with a low number of training samples with a system we call AnoNet. AnoNet's architecture is similar to CompactCNN wi…
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study
organization= Department of Physics \& Astronomy, University of British Columbia , city= Vancouver , state= BC , country= Canada；organization= Department of Integrative Oncology, BC Cancer Research Institute , city= Vancouver , state= BC , country= Canada；organization= Department of Radiology, University of British Columbia , city= Vancouver , state= BC , country= Canada
IIT Palakkad（帕拉克卡德印度理工学院）；Queen’s University Belfast（贝尔法斯特女王大学）
Time series data is ubiquitous in the real-world problems across various domains including healthcare, social media, and crime surveillance. Detecting anomalies, or irregular and rare events, in time series data, can enable us to find abnormal events in any natural phenomena, which may require special treatment. Moreover, labeled instances of anomaly are hard to get in time series data. On the other hand, time series data, due to its nature, often exhibits localized expansions and compressions in the time dimension which is called warping. These two challenges make it hard to detect anomalies …
Transfer learning (TL), the next frontier in machine learning (ML), has gained much popularity in recent years, due to the various challenges faced in ML, like the requirement of vast amounts of training data, expensive and time-consuming labelling processes for data samples, and long training duration for models. TL is useful in tackling these problems, as it focuses on transferring knowledge from previously solved tasks to new tasks. Digital twins and other intelligent systems need to utilise TL to use the previously gained knowledge and solve new tasks in a more self-reliant way, and to inc…
ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins
Department of Information Systems, University of Maryland Baltimore County（信息系统系，马里兰大学巴尔的摩县分校）；Department of Computer Science and Engineering, University at Buffalo（计算机科学与工程系，布法罗大学）；Department of Computer Science, University of Colorado Boulder（计算机科学系，科罗拉多大学波德分校）
University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）
Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner. Unlike them, the goal of our work is to fully utilize instance-level (or weak) anomaly labels, which only indicate whether any anomalous events occurred or not in each instance of temporal data. In this paper, we present WETAS, a novel framework that effectively identifies anomalous temporal segments (i.e., consecutive time points) in an input instance. WETAS learns discriminative features from the instance-level l…
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL) and monitoring of the PT for anomaly detection. We specifically investigate the application of the proposed framework to a s…
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability
McGill University（麦吉尔大学）；Georgetown University（乔治城大学）；National Cheng Kung University（国立成功大学）；National Institute of Standards and Technology（美国国家标准与技术研究院）
