Evaluation of the Potential of Deep Learning for Manufacturing Process Analytics
Research output: Thesis › Master's Thesis
Standard
2018.
Research output: Thesis › Master's Thesis
Harvard
APA
Vancouver
Author
Bibtex - Download
}
RIS (suitable for import to EndNote) - Download
TY - THES
T1 - Evaluation of the Potential of Deep Learning for Manufacturing Process Analytics
AU - Hagendorfer, Elias
N1 - no embargo
PY - 2018
Y1 - 2018
N2 - This thesis investigates the use of deep learning for the automatic identification of machine operations from multivariate time-series data emanating from sensors and actuators. Methods from deep learning and time-series analysis are reviewed with the aim of determining their suitability. A new approach is introduced to alleviate weaknesses in current approaches which include insufficient signal selection, requirement of large amount of training data or neglection of the physical nature of the system. It consists of: a preprocessing methodology based around stationarity tests, redundancy analysis and entropy measures; a deep learning algorithm classifying time series segments into operation categories; a process analytics framework dealing with operation length and frequency. The approach was applied successfully to several datasets from heavy machinery bulk handling systems.
AB - This thesis investigates the use of deep learning for the automatic identification of machine operations from multivariate time-series data emanating from sensors and actuators. Methods from deep learning and time-series analysis are reviewed with the aim of determining their suitability. A new approach is introduced to alleviate weaknesses in current approaches which include insufficient signal selection, requirement of large amount of training data or neglection of the physical nature of the system. It consists of: a preprocessing methodology based around stationarity tests, redundancy analysis and entropy measures; a deep learning algorithm classifying time series segments into operation categories; a process analytics framework dealing with operation length and frequency. The approach was applied successfully to several datasets from heavy machinery bulk handling systems.
KW - deep learning
KW - time-series analysis
KW - signal selection
KW - manufacturing process
KW - sensor data
KW - Deep Learning
KW - Zeitreihenanlyse
KW - Signalauswahl
KW - Produktionsprozess
KW - Sensordaten
M3 - Master's Thesis
ER -
