Hydraulic system fault diagnosis method based on a multi-feature fusion support vector machine

Hydraulic system fault diagnosis method based on a multi-feature fusion support vector machine

Real-time fault prevention and diagnosis in hydraulic systems is one of the challenges in engineering applications. In order to solve the problems of feature extraction and pattern recognition of different types of fault signals such as leakage, blockage, and cavitation in the hydraulic system, this...

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Bibliographic Details
Main Authors: Lihua Wang, Xiao-qiang Wu, Chunyou Zhang, Hongyan Shi
Format: Article
Language:English
Published: Wiley 2018-10-01
Series:The Journal of Engineering
Subjects:
support vector machines
fault diagnosis
radial basis function networks
hydraulic systems
entropy
feature extraction
pattern recognition
signal processing
mechanical engineering computing
hydraulic system fault diagnosis method
multifeature fusion support vector machine
fault signal
stock index diagnosis
back-projection neural network fault diagnosis method
time-frequency domain
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9028

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https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9028

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