Thresholded scalogram and its applications in process fault detection

Myong K. Jeong, Di Chen, Jye Chyi Lu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Scalograms provide measures of signal energy at various frequency bands and are commonly used in decision making in many fields including signal and image processing, astronomy and metrology. This article extends the scalogram's ability for handling noisy and possibly massive data. The proposed thresholded scalogram is built on the fast wavelet transform, which can capture non-stationary changes in data patterns effectively and efficiently. The asymptotic distribution of the thresholded scalogram is derived. This leads to large sample confidence intervals that are useful in detecting process faults statistically, based on scalogram signatures. Application of the scalogram-based data mining procedure (mainly, classification and regression trees) demonstrates the potential of the proposed methods for analysing complicated signals for making engineering decisions.

Original languageEnglish (US)
Pages (from-to)231-244
Number of pages14
JournalApplied Stochastic Models in Business and Industry
Volume19
Issue number3
DOIs
StatePublished - Sep 2003
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Business, Management and Accounting(all)
  • Management Science and Operations Research

Keywords

  • Asymptotic normality
  • Classification
  • Data mining
  • Discrete wavelet transform
  • Pattern recognition
  • Scalogram
  • Signal processing

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