Anomaly Detection for Predictive Maintenance in Industry 4.0- A survey
E3S Web Conf., 170 (2020) 02007
Published online: 2020-05-28
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
361 (2026)
https://doi.org/10.1007/978-981-95-0140-3_36
Artificial intelligence and robotics in predictive maintenance: a comprehensive review
Joseph Azeta, Theodore Tochukwu Omeche, Ilesanmi Daniyan, Johnson Opeyemi Abiola, Lanre Daniyan, Humbulani Simon Phuluwa and Rumbidzai MuvunziFrontiers in Mechanical Engineering 11 (2026)
https://doi.org/10.3389/fmech.2025.1722114
Two-Stage Hybrid Deep Learning Architecture for Cross-Domain Anomaly Detection and Failure Prediction
Rahomotul Islam, Nafiza Humauara Hasan Neha, Mumtahina Ahmed, Jannatul Maua, Neha Chaudhary, M. F. Mridha, Nilanjan Dey and Satyabrata RoyIEEE Open Journal of the Computer Society 7 117 (2026)
https://doi.org/10.1109/OJCS.2025.3643329
255 (2025)
https://doi.org/10.1016/B978-0-443-26482-5.00025-0
Artificial intelligence tools for preventive maintenance in gold processing mills: A review
Victoria Makuwaza Ruvimbo, Innocent Mupona Munyaradzi and Museka Donaldi-manager’s Journal on Future Engineering and Technology 21 (1) 19 (2025)
https://doi.org/10.26634/jfet.21.1.22261
Anomaly Detection Based on Isolation Mechanisms: A Survey
Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu and Kai Ming TingMachine Intelligence Research 22 (5) 849 (2025)
https://doi.org/10.1007/s11633-025-1554-4
Review of Process Monitoring and Anomaly Detection Applications for CNC Milling Machines in Highly Flexible Production Environments
Robin Ströbel, Marcus Mau, Marcel Diebold, Alexander Puchta and Jürgen FleischerJournal of Machine Engineering 25 (4) 33 (2025)
https://doi.org/10.36897/jme/211609
Acoustic Anomaly Detection for Propeller Crack Defects: A Drone-Based Surrogate Dataset for UAM
Juho Lee, Donghyun Yoon, Gu Moon Jeong and Hyeoncheol KimIEEE Access 13 195564 (2025)
https://doi.org/10.1109/ACCESS.2025.3632030
Lean Data – Anwendungsspezifische Reduktion großer Datenmengen im Produktionsumfeld
Marina Baucks, Marcus Mau, Peter Ruppelt, Alexander Puchta and Jürgen FleischerZeitschrift für wirtschaftlichen Fabrikbetrieb 120 (s1) 152 (2025)
https://doi.org/10.1515/zwf-2024-0148
1378 347 (2025)
https://doi.org/10.1007/978-981-96-5781-0_29
Advanced virtual metrology using distribution alignment loss for practical quality control
Yu Gyeong Ji, In Ho Kim, Minjeong Shin, Gyeong Taek Lee and Jaeyeon JangInternational Journal of Production Research 63 (23) 9051 (2025)
https://doi.org/10.1080/00207543.2025.2532147
313 (2025)
https://doi.org/10.1007/978-3-031-83097-6_14
1 (2025)
https://doi.org/10.1109/IJCNN64981.2025.11228152
Constraint guided autoencoders for joint optimization of condition indicator estimation and anomaly detection in machine condition monitoring
Maarten Meire, Quinten Van Baelen, Ted Ooijevaar and Peter KarsmakersMachine Learning 114 (7) (2025)
https://doi.org/10.1007/s10994-025-06779-0
1 (2025)
https://doi.org/10.1109/ETFA65518.2025.11205752
Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification
R. Vijay Anand, G. Magesh, I. Alagiri, Madala Guru Brahmam, C. Senthil Kumar, M. Kesavan and Azween Bin AbdullahTransactions on Emerging Telecommunications Technologies 36 (9) (2025)
https://doi.org/10.1002/ett.70224
725 43 (2025)
https://doi.org/10.1007/978-3-031-81888-2_3
Analyzing the impact of edge, fog and cloud computing on predictive maintenance in the Industrial Internet of Things
Dimple Kapoor, Deepali Gupta and Mudita UppalDiscover Computing 28 (1) (2025)
https://doi.org/10.1007/s10791-025-09653-8
Video Anomaly Detection Utilizing Efficient Spatiotemporal Feature Fusion with 3D Convolutions and Long Short‐Term Memory Modules
Sareer Ul Amin, Bumsoo Kim, Yonghoon Jung, Sanghyun Seo and Sangoh ParkAdvanced Intelligent Systems 6 (7) (2024)
https://doi.org/10.1002/aisy.202300706
(2024)
https://doi.org/10.2139/ssrn.4768353
1890 (2024)
https://doi.org/10.1109/ICETSIS61505.2024.10459473
1 (2024)
https://doi.org/10.1109/ECAI61503.2024.10607431
Tool Condition Monitoring Methods Applicable in the Metalworking Process
Melvin Alexis Lara de Leon, Jakub Kolarik, Radek Byrtus, Jiri Koziorek, Petr Zmij and Radek MartinekArchives of Computational Methods in Engineering 31 (1) 221 (2024)
https://doi.org/10.1007/s11831-023-09979-w
209 (2024)
https://doi.org/10.4018/979-8-3693-0413-6.ch008
Unsupervised Anomaly Detection Process Using LLE and HDBSCAN by Style-GAN as a Feature Extractor
Taeheon Lee, Yoonseok Kim, Youngjoo Hyun, Jeonghoon Mo and Youngjun YooInternational Journal of Precision Engineering and Manufacturing 25 (1) 51 (2024)
https://doi.org/10.1007/s12541-023-00908-2
704 (2024)
https://doi.org/10.1109/ISML60050.2024.11007348
Enhancing resilience in complex energy systems through real-time anomaly detection: a systematic literature review
Ali Aghazadeh Ardebili, Oussama Hasidi, Ahmed Bendaouia, Adem Khalil, Sabri Khalil, Dalila Luceri, Antonella Longo, El Hassan Abdelwahed, Sara Qassimi and Antonio FicarellaEnergy Informatics 7 (1) (2024)
https://doi.org/10.1186/s42162-024-00401-8
1691 (2024)
https://doi.org/10.1109/ICMLA61862.2024.00261
Anomaly detection and virtual reality visualisation in supercomputers
David Mulero-Pérez, Manuel Benavent-Lledó, Jorge Azorín-López, Diego Marcos-Jorquera and José García-RodríguezThe International Journal of Advanced Manufacturing Technology 133 (1-2) 935 (2024)
https://doi.org/10.1007/s00170-023-11255-x
Robust regression modelling for inflation factor in the Indonesian economy development
A’yunin Sofro, Ika Aprilia Rizka Azzahro, Khusnia Nurul Khikmah, Orasa Nunkaw, N. Suprapto, B.K. Prahani, M. Satriawan, H.P.A. Tjahyaningtijas, M. Abdul Ghofur and S. AndariE3S Web of Conferences 513 01006 (2024)
https://doi.org/10.1051/e3sconf/202451301006
14516 250 (2024)
https://doi.org/10.1007/978-3-031-58502-9_17
Conception and evaluation of anomaly detection models for monitoring analytical parameters in wastewater treatment plants
Pedro Oliveira, M. Salomé Duarte, Paulo Novais, Ana Cristina Bicharra Garcia and Mariza FerroAI Communications 37 (3) 443 (2024)
https://doi.org/10.3233/AIC-230064
Anomaly Detection in Binary Time Series Data: An unsupervised Machine Learning Approach for Condition Monitoring
Gábor Princz, Masoud Shaloo and Selim ErolProcedia Computer Science 232 1065 (2024)
https://doi.org/10.1016/j.procs.2024.01.105
887 259 (2024)
https://doi.org/10.1007/978-3-031-74491-4_21
14880 287 (2024)
https://doi.org/10.1007/978-981-97-5678-0_25
A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing
Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim and Jongpil JeongMachines 13 (1) 21 (2024)
https://doi.org/10.3390/machines13010021
1 (2024)
https://doi.org/10.1109/ICCCNT61001.2024.10725393
33 (2024)
https://doi.org/10.1109/ICPHM61352.2024.10627326
State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies
Sizhe Ma, Katherine A. Flanigan and Mario BergésAdvanced Engineering Informatics 62 102800 (2024)
https://doi.org/10.1016/j.aei.2024.102800
1 (2024)
https://doi.org/10.1109/CONECCT62155.2024.10677203
Enhanced Anomaly Detection in Compressor Components Using Deep Learning and an Attribute Updating Model
Guotao Yang, Shaolin Hu and Longtao WangIndustrial & Engineering Chemistry Research 63 (42) 18027 (2024)
https://doi.org/10.1021/acs.iecr.4c02007
Electric vehicle supply equipment monitoring and early fault detection through autoencoders
Maciej Sakwa, Alfredo Nespoli, Silvana Matrone, Sonia Leva, Alice Guerini, Andrea Demartini and Emanuele OgliariSustainable Energy, Grids and Networks 40 101497 (2024)
https://doi.org/10.1016/j.segan.2024.101497
Machine Learning-Driven Maintenance Order Generation in Assembly Lines
Gábor Princz, Masoud Shaloo, Fabian Reisacher and Selim ErolIFAC-PapersOnLine 58 (19) 139 (2024)
https://doi.org/10.1016/j.ifacol.2024.09.119
Enhancing Industrial Anomaly Detection with Auto Encoder-Based Temporal Convolutional Networks for Motor Fault Classification
B. D. Varalakshmi and G. M. LingarajuSN Computer Science 5 (8) (2024)
https://doi.org/10.1007/s42979-024-03425-9
Feasibility of Digital Twins to Manage the Operational Risks in the Production of a Ready-Mix Concrete Plant
Vihan Weerapura, Ranil Sugathadasa, M. Mavin De Silva, Izabela Nielsen and Amila ThibbotuwawaBuildings 13 (2) 447 (2023)
https://doi.org/10.3390/buildings13020447
1 (2023)
https://doi.org/10.1109/SoSE59841.2023.10178635
Anomaly Detection in Industrial Machinery Using IoT Devices and Machine Learning: A Systematic Mapping
Sérgio F. Chevtchenko, Elisson Da Silva Rocha, Monalisa Cristina Moura Dos Santos, Ricardo Lins Mota, Diego Moura Vieira, Ermeson Carneiro De Andrade and Danilo Ricardo Barbosa De AraújoIEEE Access 11 128288 (2023)
https://doi.org/10.1109/ACCESS.2023.3333242
Transformer-based contrastive learning framework for image anomaly detection
Wentao Fan, Weimin Shangguan and Yewang ChenInternational Journal of Machine Learning and Cybernetics 14 (10) 3413 (2023)
https://doi.org/10.1007/s13042-023-01840-7
An approach for assessing industrial IoT data sources to determine their data trustworthiness
Harald Foidl and Michael FeldererInternet of Things 22 100735 (2023)
https://doi.org/10.1016/j.iot.2023.100735
690 634 (2023)
https://doi.org/10.1007/978-3-031-43666-6_43
Anomaly Detection Methods for Industrial Applications: A Comparative Study
Maria Antonietta Panza, Marco Pota and Massimo EspositoElectronics 12 (18) 3971 (2023)
https://doi.org/10.3390/electronics12183971
1 (2023)
https://doi.org/10.1109/ICECCME57830.2023.10252853
975 (2023)
https://doi.org/10.23919/ICCAS59377.2023.10316874
Decision-making for the anomalies in IIoTs based on 1D convolutional neural networks and Dempster–Shafer theory (DS-1DCNN)
Tuğrul Çavdar, Nader Ebrahimpour, Muhammet Talha Kakız and Faruk Baturalp GünayThe Journal of Supercomputing 79 (2) 1683 (2023)
https://doi.org/10.1007/s11227-022-04739-2
Advances on Mechanics, Design Engineering and Manufacturing IV
Francesco Bianconi, Paolo Conti, Elisabetta Maria Zanetti and Giulia PascolettiLecture Notes in Mechanical Engineering, Advances on Mechanics, Design Engineering and Manufacturing IV 793 (2023)
https://doi.org/10.1007/978-3-031-15928-2_69
Anomaly Detection of DC Nut Runner Processes in Engine Assembly
James Simon Flynn, Cinzia Giannetti and Hessel Van DijkAI 4 (1) 234 (2023)
https://doi.org/10.3390/ai4010010
International Conference on Advanced Intelligent Systems for Sustainable Development
Oumaima El Hairech and Abdelouahid LyhyaouiLecture Notes in Networks and Systems, International Conference on Advanced Intelligent Systems for Sustainable Development 712 296 (2023)
https://doi.org/10.1007/978-3-031-35251-5_28
Computational Intelligence, Data Analytics and Applications
Ümit Dilbaz and Mustafa Özgür CingizLecture Notes in Networks and Systems, Computational Intelligence, Data Analytics and Applications 643 370 (2023)
https://doi.org/10.1007/978-3-031-27099-4_29
Towards achieving a high degree of situational awareness and multimodal interaction with AR and semantic AI in industrial applications
Juan Izquierdo-Domenech, Jordi Linares-Pellicer and Jorge Orta-LopezMultimedia Tools and Applications 82 (10) 15875 (2023)
https://doi.org/10.1007/s11042-022-13803-1
Data-driven predictive maintenance framework for railway systems
Jorge Meira, Bruno Veloso, Verónica Bolón-Canedo, Goreti Marreiros, Amparo Alonso-Betanzos and João GamaIntelligent Data Analysis 27 (4) 1087 (2023)
https://doi.org/10.3233/IDA-226811
Proactive Fault Diagnosis of a Radiator: A Combination of Gaussian Mixture Model and LSTM Autoencoder
Jeong-Geun Lee, Deok-Hwan Kim and Jang Hyun LeeSensors 23 (21) 8688 (2023)
https://doi.org/10.3390/s23218688
1 (2023)
https://doi.org/10.1109/ICM54990.2023.10101971
1 (2023)
https://doi.org/10.1109/AMCAI59331.2023.10431528
4516 (2023)
https://doi.org/10.1109/GLOBECOM54140.2023.10437317
367 (2023)
https://doi.org/10.1109/DCOSS-IoT58021.2023.00066
1 (2023)
https://doi.org/10.1109/SWC57546.2023.10448698
1 (2023)
https://doi.org/10.1109/ICRAI57502.2023.10089572
Explainable AI for Bearing Fault Prognosis Using Deep Learning Techniques
Deva Chaitanya Sanakkayala, Vijayakumar Varadarajan, Namya Kumar, Karan, Girija Soni, Pooja Kamat, Satish Kumar, Shruti Patil and Ketan KotechaMicromachines 13 (9) 1471 (2022)
https://doi.org/10.3390/mi13091471
Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors
Zhi Li, Fei Fei and Guanglie ZhangSensors 22 (15) 5901 (2022)
https://doi.org/10.3390/s22155901
Industrial Needs in the Fields of Artificial Intelligence, Internet of Things and Edge Computing
Dorota Stadnicka, Jarosław Sęp, Riccardo Amadio, Daniele Mazzei, Marios Tyrovolas, Chrysostomos Stylios, Anna Carreras-Coch, Juan Alfonso Merino, Tomasz Żabiński and Joan NavarroSensors 22 (12) 4501 (2022)
https://doi.org/10.3390/s22124501
207 (2022)
https://doi.org/10.1109/IRC55401.2022.00042
Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency
Shashank Chauhan and Sudong LeeIEEE Access 10 132062 (2022)
https://doi.org/10.1109/ACCESS.2022.3230352
Developing an Anomaly Detection System for Automatic Defective Products’ Inspection
Yu-Hsin HungProcesses 10 (8) 1476 (2022)
https://doi.org/10.3390/pr10081476
Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition
B. D. Deebak and Fadi Al‐TurjmanInternational Journal of Intelligent Systems 37 (12) 10289 (2022)
https://doi.org/10.1002/int.22493
Transfer Learning Auto-Encoder Neural Networks for Anomaly Detection of DDoS Generating IoT Devices
Unsub Shafiq, Muhammad Khuram Shahzad, Muhammad Anwar, et al.Security and Communication Networks 2022 1 (2022)
https://doi.org/10.1155/2022/8221351
992 (2022)
https://doi.org/10.1109/ICCA54724.2022.9831858
1 (2022)
https://doi.org/10.1109/ITC-CSCC55581.2022.9894926
881 (2022)
https://doi.org/10.1109/CCTA49430.2022.9966138
An Approach for Assessing Industrial Iot Data Sources to Determine Their Data Trustworthiness
Harald Foidl and Michael FeldererSSRN Electronic Journal (2022)
https://doi.org/10.2139/ssrn.4069988
Detecting and Processing Anomalies in a Factory of the Future
Linda Feeken, Esther Kern, Alexander Szanto, Alexander Winnicki, Ching-Yu Kao, Björn Wudka, Matthias Glawe, Elham Mirzaei, Philipp Borchers and Christian BurghardtApplied Sciences 12 (16) 8181 (2022)
https://doi.org/10.3390/app12168181
1 (2022)
https://doi.org/10.1109/ANDESCON56260.2022.9989522
(2022)
https://doi.org/10.21203/rs.3.rs-2136936/v1
MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach
Milot Gashi, Heimo Gursch, Hannes Hinterbichler, Stefan Pichler, Stefanie Lindstaedt and Stefan ThalmannSensors 22 (8) 2837 (2022)
https://doi.org/10.3390/s22082837
A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms
Abebe Diro, Naveen Chilamkurti, Van-Doan Nguyen and Will HeyneSensors 21 (24) 8320 (2021)
https://doi.org/10.3390/s21248320
Application of Enhanced CPC for Load Identification, Preventive Maintenance and Grid Interpretation
Netzah Calamaro, Avihai Ofir and Doron ShmilovitzEnergies 14 (11) 3275 (2021)
https://doi.org/10.3390/en14113275
Level Crossing Barrier Machine Faults and Anomaly Detection with the Use of Motor Current Waveform Analysis
Damian Grzechca, Paweł Rybka and Roman PawełczykEnergies 14 (11) 3206 (2021)
https://doi.org/10.3390/en14113206
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products
Vaibhav Kadam, Satish Kumar, Arunkumar Bongale, Seema Wazarkar, Pooja Kamat and Shruti PatilApplied System Innovation 4 (2) 34 (2021)
https://doi.org/10.3390/asi4020034
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
Kukjin Choi, Jihun Yi, Changhwa Park and Sungroh YoonIEEE Access 9 120043 (2021)
https://doi.org/10.1109/ACCESS.2021.3107975
Context-Based Resilience in Cyber-Physical Production System
Ada Bagozi, Devis Bianchini and Valeria De AntonellisData Science and Engineering 6 (4) 434 (2021)
https://doi.org/10.1007/s41019-021-00172-2
201 (2021)
https://doi.org/10.1109/ICMIMT52186.2021.9476186
Predictive maintenance of abnormal wind turbine events by using machine learning based on condition monitoring for anomaly detection
Huan Chen, Jyh-Yih Hsu, Jia-You Hsieh, et al.Journal of Mechanical Science and Technology 35 (12) 5323 (2021)
https://doi.org/10.1007/s12206-021-1105-z
Digital Twin-driven online anomaly detection for an automation system based on edge intelligence
Huiyue Huang, Lei Yang, Yuanbin Wang, Xun Xu and Yuqian LuJournal of Manufacturing Systems 59 138 (2021)
https://doi.org/10.1016/j.jmsy.2021.02.010
Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0
Pavol Tanuska, Lukas Spendla, Michal Kebisek, Rastislav Duris and Maximilian StremySensors 21 (7) 2376 (2021)
https://doi.org/10.3390/s21072376