Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Tawfik Thelaidjia"'
Publikováno v:
The International Journal of Advanced Manufacturing Technology. 125:5541-5556
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31497d4c3b968507ff3a6a929cd21fd7
https://doi.org/10.1007/s00170-023-10968-3
https://doi.org/10.1007/s00170-023-10968-3
Publikováno v:
The Journal of Supercomputing. 79:7014-7036
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eb779a9a29d4116fc6bdf9d20a3de9fe
https://doi.org/10.1007/s11227-022-04931-4
https://doi.org/10.1007/s11227-022-04931-4
Publikováno v:
European Journal of Electrical Engineering. 24:171-183
Induction machine health monitoring is considered a developing technology for the online detection of faults that occur even at the initial stage. The objective of this study is to present an artificial intelligence (AI) technique for the detection a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::959ab03243888d2bee9afa2f0d9b2570
https://doi.org/10.18280/ejee.240402
https://doi.org/10.18280/ejee.240402
In this paper we are interested in developing a new approach that combines successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis. Firstly, vibration signals are pre-processe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b9ea2b08d3eda1d630c6fedc16d0424
https://doi.org/10.21203/rs.3.rs-1899230/v1
https://doi.org/10.21203/rs.3.rs-1899230/v1
Publikováno v:
2019 1st International Conference on Sustainable Renewable Energy Systems and Applications (ICSRESA).
With the aim of better identify the running conditions of bearings in wind turbine, a two stages classifier approach is suggested. Firstly, a combined feature set is generated through the application of two techniques: statistical time domain paramet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c5448a04da6149f71f91fed803486b5
https://doi.org/10.1109/icsresa49121.2019.9182688
https://doi.org/10.1109/icsresa49121.2019.9182688
Feature extraction and optimized support vector machine for severity fault diagnosis in ball bearing
Publikováno v:
Engineering Solid Mechanics. :167-176
In this paper, a method for severity fault diagnosis of ball bearings is presented. The method is based on wavelet packet transform (WPT), statistical parameters, principal component analysis (PCA) and support vector machine (SVM). The key to bearing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b44ecb843d0b49b117b40e403ea16298
https://doi.org/10.5267/j.esm.2016.6.004
https://doi.org/10.5267/j.esm.2016.6.004
Publikováno v:
International Journal of Advanced Mechatronic Systems. 8:116
In this paper, a new approach is suggested for isolated and combined mechanical faults diagnosis. The suggested approach consists of two main steps: vibration signal denoising and characteristic frequency extracting. Firstly, an optimal wavelet multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a254ab40498d3c1adad9ed4334541830
https://doi.org/10.1504/ijamechs.2020.10033406
https://doi.org/10.1504/ijamechs.2020.10033406
Publikováno v:
International Journal of Data Analysis Techniques and Strategies. 11:115
The paper deals with the development of a novel feature selection approach for bearing fault diagnosis to overcome drawbacks of the distance evaluation technique (DET); one of the well-established feature selection approaches. Its drawbacks are the i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abbb8f083625c6581201d361601f28b5
https://doi.org/10.1504/ijdats.2019.098817
https://doi.org/10.1504/ijdats.2019.098817
Bearing fault diagnosis based on independent component analysis and optimized support vector machine
Publikováno v:
2015 7th International Conference on Modelling, Identification and Control (ICMIC).
This study concerns with fault diagnosis in rolling bearings using discrete wavelet transform (DWT), statistical parameters, independent component analysis (ICA) and support vector machine (SVM). The features for classification are extracted through
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2acf8340fd6ec55c7f07204eb09d7f02
https://doi.org/10.1109/icmic.2015.7409362
https://doi.org/10.1109/icmic.2015.7409362