Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis
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...
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MDPI AG
2018-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/8/12/2656 |
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| author | Wahyu Caesarendra Mahardhika Pratama Buyung Kosasih Tegoeh Tjahjowidodo Adam Glowacz |
| author_facet | Wahyu Caesarendra Mahardhika Pratama Buyung Kosasih Tegoeh Tjahjowidodo Adam Glowacz |
| author_sort | Wahyu Caesarendra |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T16:33:10Z |
| format | Article |
| id | doaj.art-0ab3c942666340a2a86bef3e8622bcbb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-13T16:33:10Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0ab3c942666340a2a86bef3e8622bcbb2022-12-21T23:38:27ZengMDPI AGApplied Sciences2076-34172018-12-01812265610.3390/app8122656app8122656Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing PrognosisWahyu Caesarendra0Mahardhika Pratama1Buyung Kosasih2Tegoeh Tjahjowidodo3Adam Glowacz4Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei DarussalamSchool of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, SingaporeSchool of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Northfields Ave, Wollongong, NSW 2522, AustraliaSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, SingaporeDepartment of Automatic Control and Robotics, AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Kraków, PolandIn 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.https://www.mdpi.com/2076-3417/8/12/2656PANFISprognosisslew bearingvibration |
| spellingShingle | Wahyu Caesarendra Mahardhika Pratama Buyung Kosasih Tegoeh Tjahjowidodo Adam Glowacz Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis Applied Sciences PANFIS prognosis slew bearing vibration |
| title | Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis |
| title_full | Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis |
| title_fullStr | Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis |
| title_full_unstemmed | Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis |
| title_short | Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis |
| title_sort | parsimonious network based on a fuzzy inference system panfis for time series feature prediction of low speed slew bearing prognosis |
| topic | PANFIS prognosis slew bearing vibration |
| url | https://www.mdpi.com/2076-3417/8/12/2656 |
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