Non-stationary Fault Diagnosis Based on Local-Wave Neural Network
Z Wang, J Li, Z Ding, Y Song - International Symposium on Neural …, 2004 - Springer
Z Wang, J Li, Z Ding, Y Song
International Symposium on Neural Networks, 2004•SpringerIn view of non-stationary and non-linearity of vibration signal from machine surface, a new
method, which is called Local-Wave Method (LWM), is presented to decompose it into
number of Intrinsic Mode Weighs (IMW). Then improved RBF network model is constructed
and trained using IMW as inputs. Taking the diesel fault diagnosis as an example, the
method, which is checked through theory and practice, provides a power means for
condition monitoring and fault diagnosis for the diesel engine.
method, which is called Local-Wave Method (LWM), is presented to decompose it into
number of Intrinsic Mode Weighs (IMW). Then improved RBF network model is constructed
and trained using IMW as inputs. Taking the diesel fault diagnosis as an example, the
method, which is checked through theory and practice, provides a power means for
condition monitoring and fault diagnosis for the diesel engine.
Abstract
In view of non-stationary and non-linearity of vibration signal from machine surface, a new method, which is called Local-Wave Method (LWM), is presented to decompose it into number of Intrinsic Mode Weighs (IMW). Then improved RBF network model is constructed and trained using IMW as inputs. Taking the diesel fault diagnosis as an example, the method, which is checked through theory and practice, provides a power means for condition monitoring and fault diagnosis for the diesel engine.
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