Szczegóły publikacji
Opis bibliograficzny
A review of reliability and fault analysis methods for heavy equipment and their components used in mining / Prerita Odeyar, Derek B. Apel, Robert Hall, Brett Zon, Krzysztof SKRZYPKOWSKI // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073 . — 2022 — vol. 15 iss. 17 art. no. 6263, s. 1–27. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 23–27, Abstr. — Publikacja dostępna online od: 2022-08-28
Autorzy (5)
- Odeyar Prerita
- Apel Derek B.
- Hall Robert
- Zon Brett
- AGHSkrzypkowski Krzysztof
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 141817 |
|---|---|
| Data dodania do BaDAP | 2022-09-17 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en15176263 |
| Rok publikacji | 2022 |
| Typ publikacji | przegląd |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
To achieve a targeted production level in mining industries, all machine systems and their subsystems must perform efficiently and be reliable during their lifetime. Implications of equipment failure have become more critical with the increasing size and intricacy of the machinery. Appropriate maintenance planning reduces the overall maintenance cost, increases machine life, and results in optimized life cycle costs. Several techniques have been used in the past to predict reliability, and there’s always been scope for improvement of the same. Researchers are finding new methods for better analysis of faults and reliability from traditional statistical methods to applying artificial intelligence. With the advancement of Industry 4.0, the mining industry is steadily moving towards the predictive maintenance approach to correct potential faults and increase equipment reliability. This paper attempts to provide a comprehensive review of different statistical techniques that have been applied for reliability and fault prediction from both theoretical aspects and industrial applications. Further, the advantages and limitations of the algorithm are discussed, and the efficiency of new ML methods are compared to the traditional methods used.
