Szczegóły publikacji
Opis bibliograficzny
Parsimonious network based on a fuzzy inference system (PANFIS) for time series feature prediction of low speed slew bearing prognosis / Wahyu Caesarendra, Mahardhika Pratama, Buyung Kosasih, Tegoeh Tjahjowidodo, Adam GŁOWACZ // Applied Sciences (Basel) [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2076-3417. — 2018 — vol. 8 iss. 12 art. no. 2656, s. 1–21. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 19–21, Abstr. — Publikacja dostępna online od: 2018-12-17
Autorzy (5)
- Caesarendra Wahyu
- Pratama Mahardhika
- Kosasih Buyung
- Tjahjowidodo Tegoeh
- AGHGłowacz Adam
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 119120 |
|---|---|
| Data dodania do BaDAP | 2019-01-09 |
| Tekst źródłowy | URL |
| DOI | 10.3390/app8122656 |
| Rok publikacji | 2018 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Applied Sciences (Basel) |
Abstract
In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic approaches, supporting online lifelong prognostics without the requirement of a retraining or reconfiguration phase. The PANFIS method is applied to normal-to-failure bearing vibration data collected for 139 days to predict the time-domain features of vibration slew bearing signals. The performance of the proposed method is compared to some established methods, such as ANFIS, eTS, and Simp_eTS. From the results, it is suggested that PANFIS offers an outstanding performance compared to those methods.
