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Titre: | Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis |
Auteur(s): | Touzout, Walid Benazzouz, Djamel Gougam, Fawzi Afia, Adel Rahmoune, Chemseddine |
Mots-clés: | Bearings Vibration signal Diagnostics Time synchronous averaging |
Date de publication: | 2020 |
Editeur: | Sage journals |
Collection/Numéro: | Advances in Mechanical Engineering , Vol. 12, N°12 (2020);pp. 1–13 |
Résumé: | Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing
techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features
extraction with most relevant information from experimental vibration signals under variable operation conditions is still
regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with
decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a
new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value
Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers
the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to
reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are
applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained
matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented
to provide a powerful automatic tool for features classification. |
URI/URL: | https://doi.org/10.1177/1687814020980569 http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7046 |
Collection(s) : | Publications Internationales
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