Gear pitting fault diagnosis using disentangled features from unsupervised deep learning

Yongzhi Qu, Yue Zhang, Miao He, David He, Chen Jiao, Zude Zhou

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Effective feature extraction is critical for machinery fault diagnosis and prognosis. The use of time–frequency features for machinery fault diagnosis has prevailed in the last decade. However, more attentions have been drawn to machine learning–based features. While time–frequency domain features can be directly correlated to fault types and fault levels, data-driven features are typically abstract representations. Therefore, classical machine learning approaches require large amount of training data to classify these abstract features for fault diagnosis. This article proposed a fully unsupervised feature extraction method for “meaningful” feature mining, named disentangled tone mining. It is shown that disentangled tone mining can effectively extract the hidden “trend” associated with machinery health state, which can be used directly for online anomaly detection and prediction. Compared with wavelet transform and time domain statistics, disentangled tone mining can better extract fault-related features and reflect the fault degradation process. Shallow methods, such as principal component analysis, multidimensional scaling and single-layer sparse autoencoder, are shown to be inferior in terms of disentangled feature learning for machinery signals. Simulation analysis is also provided to demonstrate and explain the potential mechanism underlying the proposed method.

Original languageEnglish (US)
Pages (from-to)719-730
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Volume233
Issue number5
DOIs
StatePublished - Oct 1 2019
Externally publishedYes

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is partially supported by Natural Science Foundation of China under project no. 51505353.

Publisher Copyright:
© IMechE 2019.

Keywords

  • Gear fault
  • deep learning
  • pitting
  • sparse autoencoder
  • trending analysis
  • wavelet analysis

Fingerprint

Dive into the research topics of 'Gear pitting fault diagnosis using disentangled features from unsupervised deep learning'. Together they form a unique fingerprint.

Cite this