An ANFIS-based Framework for the Prediction of Bearing’s Remaining Useful Life

Autor: Abdel wahhab Lourari, Tarak Benkedjouh, Bilal El Yousfi, Abdenour Soualhi

Publikováno v: International Journal of Prognostics and Health Management, Vol 15, Iss 1 (2024)

Bearings are critical components extensively used in rotary machines, often being the leading cause of unexpected machine shutdowns. To mitigate system failures, it is crucial to implement effective maintenance strategies. This paper introduces a nov

Externí odkaz: https://doaj.org/article/6a1e0e4ec3d6446da532be9e8784e53a

Bearing Fault Event-Triggered Diagnosis Using a Variational Mode Decomposition-Based Machine Learning Approach

Autor: Yassine Amirat, Houssem Habbouche, Mohamed Benbouzid, Tarak Benkedjouh

Publikováno v: IEEE Transactions on Energy Conversion
IEEE Transactions on Energy Conversion, Institute of Electrical and Electronics Engineers, 2021, pp.1-9. ⟨10.1109/TEC.2021.3085909⟩

International audience; The monitoring of rolling element bearing is indexed as a critical task for condition-based maintenance in various industrial applications. It allows avoiding unscheduled maintenance operations while decreasing their cost. For

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afaf40bde3c3b7ed37f96e322a08ecfb
https://doi.org/10.1109/tec.2021.3085909

Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition

Autor: Tarak Benkedjouh, Houssem Habbouche, Noureddine Zerhouni

Publikováno v: The International Journal of Advanced Manufacturing Technology. 114:145-157

Bearing is one of the most sensitive components widely used in rotary machines and main cause for unexpected breakdown in rotating machinery. Bearing failure can lead to a lengthy downtime of the machine. Accurately predicting the damage trend of bea

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::14d6a58962cac3b476b2531a7d03f67f
https://doi.org/10.1007/s00170-021-06814-z

Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

Autor: Redouane Drai, Soufiane Laddada, M.O. Si-Chaib, Tarak Benkedjouh

Publikováno v: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 234:1057-1068

In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availabili

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ec2e46082947f6070b49e5adeb733011
https://doi.org/10.1177/0954406219888544

Remaining useful life prediction of cutting tools using wavelet packet transform and extreme learning machine

Autor: R. Drai, M. O. Si Chaib, S. Laddada, Tarak Benkedjouh

Publikováno v: Algerian Journal of Signals and Systems. 3:156-165

Online tool wear prediction is a determining factor to the success of smart manufacturing operations. The implementation of sensors based Prognostic and Health Management (PHM) system plays an important role in estimating Remaining Useful Life (RUL)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::20609fab488286b47a13c9444478335a
https://doi.org/10.51485/ajss.v3i4.72