TITLE:
Fault Diagnosis of Overflow Valve Based on Trispectrum
AUTHORS:
Wenbing Wu
KEYWORDS:
Speed Control Valve, Trispectrum Two-Dimensional Slice, Two-Dimensional Wavelet, Least Square Support Vector Machine, Fault Diagnosis
JOURNAL NAME:
World Journal of Engineering and Technology,
Vol.8 No.4,
November
20,
2020
ABSTRACT: The
high-order spectrum can effectively remove Gaussian noise. The three-spectrum
and its slices represent random signals from a higher probability structure. It
can not only qualitatively describe the linearity and nonlinearity of vibration
signals closely related to mechanical failures, Gaussian and non-Gaussian
Performance, and can greatly improve the accuracy of mechanical fault
diagnosis. The two-dimensional slices of trispectrum in normal and fault states
show different peak characteristics. 2-D wavelet multi-level decomposition can
effectively compress 2-D array information. Least squares support vector
machine can obtain the global optimum under limited samples, thus avoiding the
local optimum problem, and has the advantage of reducing computational
complexity. In this paper, 2-D wavelet multi-level decomposition is used to
extract features of trispectrum 2-D slices, and input LSSVM to diagnose the
fault of the pressure reducing valve, which has achieved good results.