Defect Diagnosis in Solid Rocket Motors Using Sensors and Deep Learning Networks

[1]  Erik Scheme,et al.  Regression convolutional neural network for improved simultaneous EMG control , 2019, Journal of neural engineering.

[2]  Christos Riziotis,et al.  Fiber Optic Architectures for Strain Monitoring of Solid Rocket Motors' Propellant , 2013 .

[3]  Mark Schwabacher.  Machine Learning for Rocket Propulsion Health Monitoring , 2005 .

[4]  Charles R. Farrar,et al.  A review of flaws and damage in space launch vehicles: Motors and engines , 2014 .

[5]  Craig Lopatin,et al.  Active Sensing for Monitoring the Properties of Solid Rocket Motor Propellant Grains , 2015 .

[6]  Gary D. Seidel,et al.  Structural Health Monitoring of Nanocomposite Bonded Energetic Materials Through Piezoresistive Response , 2017 .

[7]  Mohammad Modarres,et al.  Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings , 2017 .

[8]  Lizhi Sun,et al.  Detectability of Delaminations in Solid Rocket Motors with Embedded Stress Sensors , 2012, Journal of Propulsion and Power.

[9]  R. Marimuthu,et al.  Development of efficient finite elements for structural integrity analysis of solid rocket motor propellant grains , 2013 .

[10]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[11]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Timothy C. Miller,et al.  Health Monitoring and Diagnosis of Solid Rocket Motors with Bore Cracks , 2016 .

[13]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[14]  Dario Izzo,et al.  A survey on artificial intelligence trends in spacecraft guidance dynamics and control , 2018, Astrodynamics.

[15]  Mark E. Ewing,et al.  Solid Rocket Motor Reliability and Historical Failure Modes Review , 2015 .