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Evaluation of 1D CNN Autoencoders for Lithium-ion Battery Condition Assessment Using Synthetic Data

Christopher J. Valant, Jay D. Wheaton, Michael G. Thurston, Sean P. McConky, Nenad G. Nenadic
2019 Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM  
of an autoencoder-basedanomaly detector.  ...  The decision-support system,based on the sequential probability ratio test, interpretedthe anomaly generated by the autoencoder.  ...  An early successful demonstration of an autoencoder neural network for vibration data provided the first layer of Prognostics Health Monitoring (PHM) capability -anomaly detection (Japkowicz, Myers, Gluck  ... 
doi:10.36001/phmconf.2019.v11i1.876 fatcat:6sv3qxix2vfb5kusqexb5qdtki

A Deep Learning Approach for Unsupervised Failure Detection in Smart Industry (Discussion Paper)

Angelica Liguori, Giuseppe Manco, Ettore Ritacco, Massimilano Ruffolo, Salvatore Iiritano
2021 Sistemi Evoluti per Basi di Dati  
We propose an unsupervised anomaly detection model that is able to identify abnormal behavior by analysing streaming data coming from IoT sensors installed on critical devices.  ...  We experiment the proposed model on a case study aimed at the predictive maintenance of elevators where specific sensors measure the oscillations of the lift during its daily use.  ...  [18] combines an autoencoder with a heuristic-based discriminator in order to improve the interpretability of the detection.  ... 
dblp:conf/sebd/Liguori0RRI21 fatcat:i3kp3jbfuncqhaxvgaoush5if4

Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Ahmad Kamal Mohd Nor, Srinivasa Rao Pedapati, Masdi Muhammad, Víctor Leiva
2021 Sensors  
Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management.  ...  In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets.  ...  ACS, Industrial and Engineering Chemistry Research Process monitoring and diagnosis 27 [115] Alshraideh et al., 2020 Process control via random forest classification of profile signals: an application  ... 
doi:10.3390/s21238020 pmid:34884024 fatcat:lr4zpcrmgfaupimfiulrd7koea

On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach [article]

Weizhong Yan, Lijie Yu
2019 arXiv   pre-print
One popular means of detecting combustor abnormalities is through continuously monitoring exhaust gas temperature profiles.  ...  Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in preventing costly unplanned  ...  ACKNOWLEDGEMENT We would like to thank one of our colleagues, Dr. Johan Reimann, for insightful discussion of deep learning technology over the course of this study.  ... 
arXiv:1908.09238v1 fatcat:wroexbquw5h57ocgz7qhgfp244

Deep autoencoder architecture for bridge damage assessment using responses from several vehicles

Muhammad Zohaib Sarwar, Daniel Cantero
2021 Engineering structures  
These vehicles can be instrumented and easily integrated with the existing fleet management systems to provide information that can be used for bridge health monitoring.  ...  Vehicle-assisted monitoring is a promising alternative for rapid and low-cost bridge health monitoring compared to direct instrumentation of bridges.  ...  With progress in telemetric technology, the perspective of an on-board monitoring system for multiple vehicles managed via a centralized system opens new prospects for SHM.  ... 
doi:10.1016/j.engstruct.2021.113064 fatcat:fdlek5perbbcvopx2nuwvqmzei

E-Quarantine: A Smart Health System for Monitoring Coronavirus Patients for Remotely Quarantine [article]

Doaa Mohey El-Din, Aboul Ella Hassanein, Ehab E. Hassanien, Walaa M.E. Hussein
2020 arXiv   pre-print
This paper proposes a smart health system that monitors the patients holding the Coronavirus remotely.  ...  An increasing number of infected with this disease causes the Inability problem to fully care in hospitals and afflict many doctors and nurses inside the hospitals.  ...  The essential challenge in smart health is interpreting, fusing and visualizing big data extracted from multiple smart devices or sensors.  ... 
arXiv:2005.04187v1 fatcat:twrqpsagpjgwpmhuozg5irqnoq

Data Analytics in the Internet of Things: A Survey

Tausifa Jan Saleem, Mohammad Ahsan Chishti
2019 Scalable Computing : Practice and Experience  
Moreover, conventional procedures do not address the surging analytical demands of IoT systems.  ...  The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge.  ...  However, this should be done while preserving the accuracy of deep learning models. Visualization. Data visualization aims to make data more meaningful for further analysis and interpretation.  ... 
doi:10.12694/scpe.v20i4.1562 fatcat:y2fiya3q2bawhdg6hhczfpdbee

On-demand teleradiology using smartphone photographs as proxies for DICOM images [article]

Christine Podilchuk, Siddhartha Pachhai, Robert Warfsman, Richard Mammone
2019 arXiv   pre-print
The photographs can be conveniently taken with a smartphone and analyzed remotely by either human or AI experts. An autoencoder preprocessor is shown to improve the performance for human experts.  ...  The autoencoder preprocessor increases the PSNR by 15 dB or greater and provides an AUC that is statistically equivalent to using the original DICOM images.  ...  The proposed photographic method is also investigated for the scenario when the transmission of an image is to be interpreted by an AI expert.  ... 
arXiv:1909.05669v2 fatcat:2et6raeb6jdv7oap655jsrvbx4

Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications [article]

Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe
2020 arXiv   pre-print
Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting  ...  Health Management (PHM) applications.  ...  Acknowledgment The contributions of Olga Fink and Qin Wang were funded by the Swiss National Science Foundation (SNSF) Grant no. PP00P2 176878.  ... 
arXiv:2005.02144v1 fatcat:wxm3dstogjfkhbcu6aueyaddja

Deep Learning for Anomaly Detection: A Survey [article]

Raghavendra Chalapathy (University of Sydney and Capital Markets Cooperative Research Centre, Sanjay Chawla (Qatar Computing Research Institute
2019 arXiv   pre-print
Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness.  ...  For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains.  ...  The typical approach in credit card fraud detection is to maintain a usage profile for each user and monitor the user profiles to detect any deviations.  ... 
arXiv:1901.03407v2 fatcat:x3tb4ccxfvdkfo7k2y2oxhr7ly

Potential, challenges and future directions for deep learning in prognostics and health management applications

Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe
2020 Engineering applications of artificial intelligence  
Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting  ...  Health Management (PHM) applications.  ...  Acknowledgements The contributions of Olga Fink and Qin Wang were funded by the Swiss National Science Foundation (SNSF) Grant no. PP00P2_176878.  ... 
doi:10.1016/j.engappai.2020.103678 fatcat:6heplbhuozautml5p7pzwrlfvq

Deep learning in the construction industry: A review of present status and future innovations

Taofeek D. Akinosho, Lukumon O. Oyedele, Muhammad Bilal, Anuoluwapo O. Ajayi, Manuel Davila Delgado, Olugbenga O. Akinade, Ashraf A. Ahmed
2020 Journal of Building Engineering  
The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building  ...  To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry.  ...  support for this study.  ... 
doi:10.1016/j.jobe.2020.101827 fatcat:d5r6tsjicbdyrf7qvucaxfk25e

Deep Convolutional Variational Autoencoder as a 2D-Visualization tool for Partial Discharge Source Classification in Hydrogenerators

Ryad Zemouri, Melanie Levesque, Normand Amyot, Claude Hudon, Olivier Kokoko, Antoine Tahan
2019 IEEE Access  
INDEX TERMS Hydrogenerators, diagnosis, partial discharges, deep neural networks, convolutional variational autoencoder, data visualization, feature extraction, model interpretation, generative model.  ...  For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies.  ...  The objective of this study was to enhance the health-monitoring system for helicopters by visual data analyzing of the structural vibrations, in order to recognize different flight conditions directly  ... 
doi:10.1109/access.2019.2962775 fatcat:4zuehk4tx5b3nhc6x3and3uete

Deep learning in systems medicine

Haiying Wang, Estelle Pujos-Guillot, Blandine Comte, Joao Luis de Miranda, Vojtech Spiwok, Ivan Chorbev, Filippo Castiglione, Paolo Tieri, Steven Watterson, Roisin McAllister, Tiago de Melo Malaquias, Massimiliano Zanin (+2 others)
2020 Briefings in Bioinformatics  
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases.  ...  It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine.  ...  Other factors to be considered include further improvement of the interpretability of DL predictions and transformation of DL away its current black box model, through, for example, the visualization of  ... 
doi:10.1093/bib/bbaa237 pmid:33197934 pmcid:PMC8382976 fatcat:bjhlu5jaubci3lm4j3vxiofehu

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges [article]

Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha
2017 arXiv   pre-print
The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking.  ...  We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.  ...  IoT is new networking paradigm and it is expected to be deployed in health care, smart cities, industry, home automation, agriculture, and industry.  ... 
arXiv:1709.06599v1 fatcat:llcg6gxgpjahha6bkhsitglrsm
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