PredMaX: Predictive maintenance with explainable deep convolutional autoencoders
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[1] G. D. Fabritiis,et al. PlayMolecule Glimpse: Understanding Protein–Ligand Property Predictions with Interpretable Neural Networks , 2022, J. Chem. Inf. Model..
[2] Jinde Zheng,et al. Multi-class fuzzy support matrix machine for classification in roller bearing fault diagnosis , 2022, Adv. Eng. Informatics.
[3] Grigorios Tsoumakas,et al. VisioRed: A Visualisation Tool for Interpretable Predictive Maintenance , 2021, IJCAI.
[4] Hongkai Jiang,et al. Rolling bearing fault diagnosis using optimal ensemble deep transfer network , 2020, Knowl. Based Syst..
[5] Rodrigo da Rosa Righi,et al. Predictive maintenance in the Industry 4.0: A systematic literature review , 2020, Comput. Ind. Eng..
[6] Vikram Krishnamurthy,et al. Explainable AI Framework for Imaging-Based Predictive Maintenance for Automotive Applications and Beyond , 2020 .
[7] Yang Yu,et al. An intelligent fault diagnosis method for rotor-bearing system using small labeled infrared thermal images and enhanced CNN transferred from CAE , 2020, Adv. Eng. Informatics.
[8] Stephan Matzka,et al. Explainable Artificial Intelligence for Predictive Maintenance Applications , 2020, 2020 Third International Conference on Artificial Intelligence for Industries (AI4I).
[9] Selver Softic,et al. Explainable AI in Manufacturing: A Predictive Maintenance Case Study , 2020, APMS.
[10] Sandeep Verma,et al. Identifying NOx Sensor Failure for Predictive Maintenance of Diesel Engines using Explainable AI , 2020 .
[11] Marcel van Gerven,et al. Explainable Deep Learning: A Field Guide for the Uninitiated , 2020, J. Artif. Intell. Res..
[12] Le Li,et al. Remaining useful life prediction via a variational autoencoder and a time‐window‐based sequence neural network , 2020, Qual. Reliab. Eng. Int..
[13] Imme Ebert-Uphoff,et al. Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability , 2019, Journal of Advances in Modeling Earth Systems.
[14] Thyago P. Carvalho,et al. A systematic literature review of machine learning methods applied to predictive maintenance , 2019, Comput. Ind. Eng..
[15] Xianghua Xie,et al. TimeCluster: dimension reduction applied to temporal data for visual analytics , 2019, The Visual Computer.
[16] Federico Marini,et al. Chemometric Methods for Spectroscopy-Based Pharmaceutical Analysis , 2018, Front. Chem..
[17] Tiedo Tinga,et al. Automated Failure Diagnosis in Aviation Maintenance Using eXplainable Artificial Intelligence (XAI) , 2018 .
[18] Teng Li,et al. Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder , 2017 .
[19] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[20] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[21] Ankur Taly,et al. Axiomatic Attribution for Deep Networks , 2017, ICML.
[22] Zhihua Zhang,et al. Support Matrix Machines , 2015, ICML.
[23] Navdeep Jaitly,et al. Pointer Networks , 2015, NIPS.
[24] Józef Jonak,et al. Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform , 2015, Appl. Soft Comput..
[25] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[26] Arturo Garcia-Perez,et al. Reconfigurable Monitoring System for Time-Frequency Analysis on Industrial Equipment Through STFT and DWT , 2013, IEEE Transactions on Industrial Informatics.
[27] Jay Lee,et al. Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure , 2012, IEEE Transactions on Reliability.
[28] H. W. Ngan,et al. Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis , 2010, IEEE Transactions on Instrumentation and Measurement.
[29] V. Makis,et al. Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models , 2007 .
[30] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[31] Dale E. Seborg,et al. Optimal selection of soft sensor inputs for batch distillation columns using principal component analysis , 2005 .
[32] S. Joe Qin,et al. Multivariate process monitoring and fault diagnosis by multi-scale PCA , 2002 .
[33] Charu C. Aggarwal,et al. On the Surprising Behavior of Distance Metrics in High Dimensional Spaces , 2001, ICDT.
[34] Daniel R. Lewin,et al. Predictive maintenance using PCA , 1994 .
[35] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[36] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[37] P. D. McFadden,et al. Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .
[38] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[39] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[40] Junsheng Cheng,et al. Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis , 2021 .
[41] Baokun Han,et al. An intelligent diagnosis framework for roller bearing fault under speed fluctuation condition , 2021, Neurocomputing.
[42] Jyoti K. Sinha,et al. A future possibility of vibration based condition monitoring of rotating machines , 2013 .
[43] Eun Ryung Lee,et al. PRINCIPAL COMPONENT ANALYSIS IN VERY HIGH-DIMENSIONAL SPACES , 2012 .
[44] Yanyang Zi,et al. Bearing condition monitoring based on shock pulse method and improved redundant lifting scheme , 2008, Math. Comput. Simul..