Andere Kunden interessierten sich auch für
![Modeling and Identification for Anomaly detection in Mechanical System]( "Modeling and Identification for Anomaly detection in Mechanical System")
Modeling and Identification for Anomaly detection in Mechanical System40,76 €![Symbolic Dynamics and Controls of a Parallel Manipulator]( "Symbolic Dynamics and Controls of a Parallel Manipulator")
Symbolic Dynamics and Controls of a Parallel Manipulator38,99 €![Neural-Fuzzy Inference Engine for Maritime Anomaly Detection]( "Neural-Fuzzy Inference Engine for Maritime Anomaly Detection")
Neural-Fuzzy Inference Engine for Maritime Anomaly Detection79,00 €![Feature Selection for Anomaly Detection in Hyperspectral Data]( "Feature Selection for Anomaly Detection in Hyperspectral Data")
Feature Selection for Anomaly Detection in Hyperspectral Data44,99 €![Advanced Symbolic Analysis for VLSI Systems]( "Advanced Symbolic Analysis for VLSI Systems")
Advanced Symbolic Analysis for VLSI Systems110,99 €![Smart Cities: An Anomaly Detection in Wireless Sensor Network]( "Smart Cities: An Anomaly Detection in Wireless Sensor Network")
Smart Cities: An Anomaly Detection in Wireless Sensor Network23,99 €![Advanced Symbolic Analysis for VLSI Systems]( "Advanced Symbolic Analysis for VLSI Systems")
Advanced Symbolic Analysis for VLSI Systems110,99 €
Major catastrophic failures in large scale
engineering systems (e.g., aircraft, power plants and
turbo-machinery) can possibly be averted if the
malignant anomalies are detected at an early stage.
This dissertation experimentally validates a novel
method called Symbolic Time Series Analysis(STSA) for
anomaly detection in electromechanical systems,
derived from time series data of pertinent measured
variable(s).
In this dissertation, the performance of
this anomaly detection method is compared with that
of other existing pattern recognition techniques from
the perspectives of early detection of fatigue damage
in Al-2024. The experimental apparatus, on which the
anomaly detection method is tested, is a multi-degree
of freedom mass-beam structure excited by oscillatory
motion of two electromagnetic shakers. The evolution
of fatigue crack damage at one of the failure sites
is detected from STSA of the pertinent sensor signal.
Industrial Application-The dissertation presents STSA
of bearing acceleration derived from a dynamic
simulation model for detection and estimation of
parametric changes in flexible disc/diaphragm
couplings due to angular misalignment between shafts.
engineering systems (e.g., aircraft, power plants and
turbo-machinery) can possibly be averted if the
malignant anomalies are detected at an early stage.
This dissertation experimentally validates a novel
method called Symbolic Time Series Analysis(STSA) for
anomaly detection in electromechanical systems,
derived from time series data of pertinent measured
variable(s).
In this dissertation, the performance of
this anomaly detection method is compared with that
of other existing pattern recognition techniques from
the perspectives of early detection of fatigue damage
in Al-2024. The experimental apparatus, on which the
anomaly detection method is tested, is a multi-degree
of freedom mass-beam structure excited by oscillatory
motion of two electromagnetic shakers. The evolution
of fatigue crack damage at one of the failure sites
is detected from STSA of the pertinent sensor signal.
Industrial Application-The dissertation presents STSA
of bearing acceleration derived from a dynamic
simulation model for detection and estimation of
parametric changes in flexible disc/diaphragm
couplings due to angular misalignment between shafts.