Szczegóły publikacji

Opis bibliograficzny

Threshold lines identification for non-Gaussian distributed diagnostic features / Katarzyna Maraj-Zygmąt, Wojciech Żuławiński, Tomasz BARSZCZ, Radosław Zimroz, Agnieszka Wyłomańska // Measurement ; ISSN 0263-2241. — 2023 — vol. 221 art. no. 113495, s. 1-17. — Bibliogr. s. 16-17, Abstr. — Publikacja dostępna online od: 2023-08-28

Autorzy (5)

  • Maraj-Zygmąt Katarzyna
  • Żuławiński Wojciech
  • AGHBarszcz Tomasz
  • Zimroz Radosław
  • Wyłomańska Agnieszka

Słowa kluczowe

impulsive behaviourempirical quantile linesthreshold identificationtime varying characteristicsoutlierssegmentationautoregressive time seriesnon Gaussian distribution

Dane bibliometryczne

ID BaDAP150187
Data dodania do BaDAP2023-12-20
Tekst źródłowyURL
DOI10.1016/j.measurement.2023.113495
Rok publikacji2023
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaMeasurement

Abstract

Machine condition monitoring systems are frequently used in the industry, especially for critical infrastructure. Decision making is still challenging due to the lack of limit values (called also thresholds). Identification of thresholds is in particular difficult for unique machines with specific Health Index (HI) data properties. Our contribution is a procedure for threshold values identification for HI data with time-varying characteristics and non-Gaussian behaviour. The proposed methodology consists of few crucial steps, such as data segmentation, data modelling, and Monte Carlo simulations for quantile lines identification based on the fitted models. The proposed methods are based on robust statistics dedicated to non-Gaussian distributed data. Novelty of the paper is related to the extension of simple ”Trend+Noise” model for HI data and model-based framework for threshold lines estimation. Thus, the method provides more accurate results for non-Gaussian, time-varying data. We demonstrate the superiority of the proposed procedure over the classical ones. The presented simulation study clearly indicates the efficiency of the approach for non-Gaussian HI data. The methodology is applied to two real datasets with different properties (level of non-Gaussianity and presence of interdependence in the random part). We believe that proposed procedure could be considered as a guideline for future research and may be implemented in commercial monitoring systems.