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Techniques of trend analysis in degradation-based prognostics

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Abstract

Monotonic fault progression is an important assumption for a number of prognostic models. This assumption can be violated through human intervention and self‐healing and result in non-monotonic degradation data which not only increases the uncertainty but also may cause model failure. Methods to analyze and handle non-monotonic degradation in repairable systems are practically nonexistent in the literature. In this research, we intend to consider repairable systems in which self‐healing is possible and human interventions are desirable. We presented a novel example of self-healing for fatigue cracks analyzed by acoustic emission. The aim of the present paper is to initiate a new research area on using non-monotonic measures in degradation-based prognostics. However, this research is not a review of trend analysis techniques, and therefore, there are more techniques to be considered or developed in future studies. In effect, trend analysis should be considered as an integral part of prognostics and health management. This study considers trend analysis for three classes of data, (1) prognostic parameters, (2) degradation waveform, and (3) multivariate data. A new form of crest factor is introduced for more effective waveform analysis of non-monotonic data. In addition, two algorithms are introduced to treat non-monotonic trend. The prognostic model used in this research does not produce results without treating non-monotonicity. These kinds of algorithm have promising potential to treat non-monotonicity and deal with arbitrary stationary noise in degradation data.

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Authors and Affiliations

  1. Western New England University, Springfield, MA, USA

    Seyed A. Niknam

  2. University of Tennessee, Knoxville, TN, USA

    John Kobza & J. Wesley Hines

Authors
  1. Seyed A. Niknam
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  2. John Kobza
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  3. J. Wesley Hines
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Correspondence to Seyed A. Niknam.

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Niknam, S.A., Kobza, J. & Hines, J.W. Techniques of trend analysis in degradation-based prognostics. Int J Adv Manuf Technol 88, 2429–2441 (2017). https://doi.org/10.1007/s00170-016-8909-5

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  • DOI: https://doi.org/10.1007/s00170-016-8909-5

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