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Unsupervised Learning-Based Phm Framework to Inform Maintenance of Air Compressors in the Semiconductor Industry and an Empirical Study

https://doi.org/10.2139/ssrn.4662600
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inferredcites →https://doi.org/10.1007/978-1-4614-7138-7openalex-extracted
An Introduction to Statistical Learning
inferredcites →https://doi.org/10.1016/j.aei.2021.101404openalex-extracted
A survey of modeling for prognosis and health management of industrial equipment
inferredcites →https://doi.org/10.1016/j.asoc.2023.110176openalex-extracted
A comprehensive survey on design and application of autoencoder in deep learning
inferredcites →https://doi.org/10.1016/j.ast.2019.105423openalex-extracted
Combining multiple deep learning algorithms for prognostic and health management of aircraft
inferredcites →https://doi.org/10.1016/j.cie.2019.04.051openalex-extracted
Prognostic and health management for adaptive manufacturing systems with online sensors and flexible structures
inferredcites →https://doi.org/10.1016/j.cie.2023.109547openalex-extracted
Detecting abnormal behavior of automatic test equipment using autoencoder with event log data
inferredcites →https://doi.org/10.1016/j.measurement.2020.107929openalex-extracted
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
inferredcites →https://doi.org/10.1016/j.procir.2021.11.329openalex-extracted
Predictive Maintenance in Industry 4.0: Current Themes
inferredcites →https://doi.org/10.1016/j.rcim.2022.102441openalex-extracted
Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises
inferredcites →https://doi.org/10.1016/j.ress.2018.06.021openalex-extracted
Recent advances in prognostics and health management for advanced manufacturing paradigms
inferredcites →https://doi.org/10.1016/j.ress.2021.108140openalex-extracted
Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review
inferredcites →https://doi.org/10.1016/j.rser.2019.109405openalex-extracted
A review on prognostics and health management (PHM) methods of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.ymssp.2017.11.024openalex-extracted
A review on the application of deep learning in system health management
inferredcites →https://doi.org/10.1109/bigdata50022.2020.9378015openalex-extracted
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines
inferredcites →https://doi.org/10.1109/jas.2019.1911393openalex-extracted
Unsupervised electric motor fault detection by using deep autoencoders
inferredcites →https://doi.org/10.1109/phm2022-london52454.2022.00042openalex-extracted
Recent Research and Applications in Variational Autoencoders for Industrial Prognosis and Health Management: A Survey
inferredcites →https://doi.org/10.1109/taes.2009.5089535openalex-extracted
Prognostic Health Management of Aircraft Power Generators
inferredcites →https://doi.org/10.1109/tevc.2018.2808689openalex-extracted
Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations
inferredcites →https://doi.org/10.1109/tii.2020.3019788openalex-extracted
Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning
inferredcites →https://doi.org/10.1109/tim.2022.3212547openalex-extracted
Unsupervised Machine Anomaly Detection Using Autoencoder and Temporal Convolutional Network
inferredcites →https://doi.org/10.1145/1541880.1541882openalex-extracted
Anomaly detection
inferredcites →https://doi.org/10.1186/s42400-022-00134-9openalex-extracted
Practical autoencoder based anomaly detection by using vector reconstruction error
inferredcites →https://doi.org/10.3389/frai.2020.578613openalex-extracted
Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead
inferredcites →https://doi.org/10.48550/arxiv.2101.11539openalex-extracted
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