Application of Manifold Learning to Machinery Fault Diagnosis

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [12 references]  Display  Hide  Download

https://hal.inria.fr/hal-01614986
Contributor : Hal Ifip <>
Submitted on : Wednesday, October 11, 2017 - 4:57:38 PM
Last modification on : Thursday, February 21, 2019 - 9:30:02 AM
Long-term archiving on: Friday, January 12, 2018 - 3:47:18 PM

File

433802_1_En_5_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

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⟩

Share

Metrics

Record views

194

Files downloads

99