Abstract : The essence of machinery fault diagnosis is pattern recognition. Extracting the fault pattern contained in the vibration signal is the frequently used method to diagnose mechanical fault. Manifold Learning is widely used to extract the non-linear structure within the data and could do the dimensionality reduction of high-dimensional signal. Therefore manifold learning is employed to diagnose the machinery fault. The feature space is constructed by characters in time-frequency domain of vibration signal firstly, and then the manifold learning method named as sparse manifold clustering and embedding is used to extract the essential nonlinear structure of feature space. Afterwards, the fault diagnosis is implemented with spectral clustering and support vector machine. The experiment demonstrates that the approach can effectively diagnose the fault of Machinery.
https://hal.inria.fr/hal-01614986
Contributor : Hal Ifip
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Submitted on : Wednesday, October 11, 2017 - 4:57:38 PM
Last modification on : Thursday, February 21, 2019 - 9:30:02 AM
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Jiangping Wang, Tengfei Duan, Lujuan Lei. Application of Manifold Learning to Machinery Fault Diagnosis. 9th International Conference on Intelligent Information Processing (IIP), Nov 2016, Melbourne, VIC, Australia. pp.41-49, ⟨10.1007/978-3-319-48390-0_5⟩. ⟨hal-01614986⟩
