The ability to detect anomalies in time series is considered highly valuable in numerous application domains. The sequential nature of time series objects is responsible for an additional feature complexity, ultimately requiring specialized approaches in order to solve the task. Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series. Inspired by the success of deep learning methods in computer vision, several studies have proposed transforming time series into image-like representations, used as inputs for deep learning models, and have led to very promising results in classification tasks. In this paper, we first review the signal to image encoding approaches found in the literature. Second, we propose modifications to some of their original formulations to make them more robust to the variability in large datasets. Third, we compare them on the basis of a common unsupervised task to demonstrate how the choice of the encoding can impact the results when used in the same deep learning architecture. We thus provide a comparison between six encoding algorithms with and without the proposed modifications. The selected encoding methods are Gramian Angular Field, Markov Transition Field, recurrence plot, grey scale encoding, spectrogram, and scalogram. We also compare the results achieved with the raw signal used as input for another deep learning model. We demonstrate that some encodings have a competitive advantage and might be worth considering within a deep learning framework. The comparison is performed on a dataset collected and released by Airbus SAS, containing highly complex vibration measurements from real helicopter flight tests. The different encodings provide competitive results for anomaly detection.
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Department of Electrical and Computer Engineering, University of Victoria（电气与计算机工程系，维多利亚大学）；Department of Mechanical Engineering, University of Victoria（机械工程系，维多利亚大学）
Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University（工业控制技术国家重点实验室，控制科学与工程学院，浙江大学）；Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems（复杂系统先进控制与智能自动化湖北省重点实验室）；Department of Chemical and Materials Engineering, University of Alberta（阿尔伯塔大学化学与材料工程系）
IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring
University of British Columbia（不列颠哥伦比亚大学）；Jiangxi University of Finance and Economics（江西财经大学）；Chongqing University（重庆大学）；Institute for Infocomm Research（信息通信研究院）；Nanyang Technological University（南洋理工大学）
Supervised Learning based Method for Condition Monitoring of Overhead Line Insulators using Leakage Current Measurement
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）；MIG；Nanyang Technological University（南洋理工大学）；National Grid Plc（国家电网公司）；Newcastle University（纽卡斯尔大学）
Asset management, condition monitoring and Digital Twins: damage detection and virtual inspection on a reinforced concrete bridge
SAP Norway, Engineering Centre of Excellence（SAP挪威卓越工程中心）；NTNU, Faculty of Engineering（挪威科技大学工程学院）；Norwegian Public Roads Administration, Division of Operation and Maintenance（挪威公共道路管理局运营与维护处）
MOLA: Enhancing Industrial Process Monitoring Using Multi-Block Orthogonal Long Short-Term Memory Autoencoder
School of Chemical Engineering, Oklahoma State University（俄克拉荷马州立大学化学工程学院）；College of Chemical Engineering, Beijing University of Chemical Technology（北京化工大学化学工程学院）；Cain Department of Chemical Engineering, Louisiana State University（路易斯安那州立大学化学工程Cain学院）
Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes
North Carolina State University（北卡罗来纳州立大学）；Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University（北卡罗来纳州立大学爱德华·P·菲茨工业与系统工程系）
A novel method for error analysis in radiation thermometry with application to industrial furnaces
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)（Vicomtech基金会，巴斯克研究与技术联盟）；TECNUN School of Engineering, University of Navarra（纳瓦拉大学TECNUN工程学院）；Institute of Data Science and Artificial Intelligence, University of Navarra（纳瓦拉大学数据科学与人工智能研究所）；Petronor Innovation（Petronor创新公司）；Petróleos del Norte（北方石油公司）
TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine
Tim Langer Chair of Highly-Parallel VLSI Systems and Neuro-Microelectronics TU Dresden（高等并行非常规集成电路系统与神经微电子学教授，德累斯顿技术大学）；Fraunhofer Institute for Machine Tools and Forming Technology IWU Dresden（机械加工与成形技术弗劳恩霍夫研究所，德累斯顿）；Fraunhofer Institute for Integrated Circuits IIS Division Engineering of Adaptive Systems EAS Dresden（集成电路弗劳恩霍夫研究所，适应性系统工程部门，德累斯顿）
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions
Centre for Artificial Intelligence（人工智能中心）；ZHAW School of Engineering（苏黎世应用科技大学工程学院）；Institute of Embedded Systems（嵌入式系统研究所）；Kistler Instrumente AG（奇石乐仪器股份公司）；Institute of Neuroinformatics（神经信息学研究所）；ETH & University of Zürich（苏黎世联邦理工学院与苏黎世大学）；ZHAW Centre for Artificial Intelligence（苏黎世应用科技大学人工智能中心）；European Centre for Living Technology (ECLT)（欧洲活技术中心（ECLT））
The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
Northern Arizona University（北亚利桑那大学）；University of Electronic Science and Technology of China（电子科技大学）；Trine University（特莱恩大学）；Yantai University（烟台大学）；Northwestern University（西北大学）
Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance
School of Computer Science, Hunan University of Technology and Business（湖南工商大学计算机学院）；Xiangjiang Laboratory（湘江实验室）；School of Intelligent Software and Engineering, Nanjing University（南京大学智能软件与工程学院）；Hunan Normal University（湖南师范大学）；Department of Computer Science, Brunel University London（布鲁内尔伦敦大学计算机学院）
