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Thresholded scalogram and its applications in process fault detection

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  • Myong K. Jeong
  • Di Chen
  • Jye‐Chyi Lu

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. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • Myong K. Jeong & Di Chen & Jye‐Chyi Lu, 2003. "Thresholded scalogram and its applications in process fault detection," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 19(3), pages 231-244, July.
    • Handle: RePEc:wly:apsmbi:v:19:y:2003:i:3:p:231-244
    DOI: 10.1002/asmb.495
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    1. Hanen Chaouch & Samia Charfeddine & Sondess Ben Aoun & Houssem Jerbi & Víctor Leiva, 2022. "Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System," Mathematics, MDPI, vol. 10(6), pages 1-16, March.

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