TY  - JOUR
TI  - A new bearing fault diagnosis approach based on common spatial pattern features
AB  - Condition monitoring in machines holds significant importance for early fault detection, optimizing maintenance processes, and ensuring operational continuity. In this study, a novel intelligent detection approach for rolling bearings is introduced, utilizing the Common Spatial Pattern (CSP) method to extract distinctive features related to bearing faults. By maximizing the variance ratio of signal matrices from distinct sources, CSP sets itself apart from conventional frequency-based features. This technique captures characteristic vibration patterns unique to each measurement, enabling differentiation between faulty and healthy bearings. The effectiveness of the proposed method was assessed using Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbour (k-NN) algorithms across two diverse datasets. The results indicated an 88.5% accuracy in two-class fault detection and 93.5% in fault classification when employing ANN. Comparison with traditional time domain feature sets highlighted the superior performance of CSP features, exhibiting elevated accuracy rates in both two-class and multiclass scenarios. Thus, CSP features emerge as a promising avenue for effectively monitoring bearing conditions through vibration data.
AU  - karabacak, yunus emre
AU  - Gursel Ozmen, Nurhan
DO  - 10.28948/ngumuh.1330864
PY  - 2023
JO  - Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
VL  - 12
IS  - 4
SN  - 2564-6605
SP  - 1545
EP  - 1557
DB  - TRDizin
UR  - http://search/yayin/detay/1201941
ER  -