New acoustic monitoring method using cross-correlation of primary frequency spectrum

Huakang Li*, Yi Luo, Jie Huang, Tetsuo Kanemoto, Minyi Guo, Feilong Tang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


The acoustic data remotely measured by microphones are widely used to investigate monitoring and diagnose integrity of ball bearing in rotational machines. Early fault diagnosis is very difficult for acoustic emission. We propose a new method using a cross-correlation of frequency spectrum to classify various faults with fine grit. Principal component analysis (PCA) is used to separate the primary frequency spectrum into main frequency and residual frequency. Different with conventional classification using the PCA eigenvalue, we introduce the general cross-correlation (GCC) of main frequency and residual frequency spectrums between a basic signal vector and monitoring signal. Multi-classification strategy based on binary-tree support vector machine (SVM) is applied to perform faults diagnosis. In order to remove noise interference and increase robustness, a normalization method is proposed during time generation. Experiment results show that PCA-GCC-SVM method is able to diagnose various faults with high sensitivity.

Original languageEnglish
Pages (from-to)293-301
Number of pages9
JournalJournal of Ambient Intelligence and Humanized Computing
Issue number3
Publication statusPublished - Jun 2013
Externally publishedYes


  • Acoustic emission
  • General correlation coefficient
  • Independent component analysis
  • Principal component analysis
  • Support vector machine


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