ORCID Profile
0000-0003-0074-5101
Current Organisations
KU Leuven
,
Katholieke Universiteit Leuven
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Publisher: Elsevier BV
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Elsevier BV
Date: 06-2015
Publisher: Elsevier BV
Date: 03-2015
Publisher: Elsevier BV
Date: 2014
Publisher: IOP Publishing
Date: 04-2018
Publisher: MDPI AG
Date: 30-05-2017
DOI: 10.3390/S17061247
Publisher: Elsevier BV
Date: 12-2014
Publisher: MDPI AG
Date: 06-04-2017
DOI: 10.3390/APP7040363
Publisher: Springer International Publishing
Date: 27-11-2015
Publisher: MDPI AG
Date: 17-12-2018
DOI: 10.3390/APP8122656
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.
Publisher: Elsevier BV
Date: 02-2016
Publisher: Elsevier BV
Date: 02-2014
Publisher: MDPI AG
Date: 04-04-2017
Publisher: Elsevier BV
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
No related grants have been discovered for Tegoeh Tjahjowidodo.