شماره ركورد كنفرانس :
1532
عنوان مقاله :
Discrete Wavelet Transform, Support Vector Machine and Adaptive Neuro-Fuzzy Inference in Bearing Fault Diagnosis
عنوان به زبان ديگر :
Discrete Wavelet Transform, Support Vector Machine and Adaptive Neuro-Fuzzy Inference in Bearing Fault Diagnosis
پديدآورندگان :
Moosavian Ashkan نويسنده Tarbiat Modares University - Department of Mechanical Engineering of Agricultural Machinery , Ahmadi Hojat نويسنده Tarbiat Modares University - Department of Mechanical Engineering of Agricultural Machinery , Sakhaei Babak نويسنده Tarbiat Modares University - Department of Mechanical Engineering of Agricultural Machinery , Labbafi Reza نويسنده Tarbiat Modares University - Department of Mechanical Engineering of Agricultural Machinery , Masoudian Shahed نويسنده Tarbiat Modares University - Department of Mechanical Engineering of Agricultural Machinery
كليدواژه :
Rolling element bearing , Wavelet Transform , Adaptive neuro-fuzzy inference , Fault diagnosis , Support Vector Machine
عنوان كنفرانس :
هشتمين كنفرانس ملي نگهداري و تعميرات
چكيده لاتين :
This paper presents an useful intelligent system for bearing fault diagnosis based on vibration analysis
using discrete wavelet transform (DWT) and two classifiers, namely, support vector machine (SVM)
and adaptive neuro-fuzzy inference (ANFIS). Discrete wavelet transform with Daubechies-3 wavelet
was used to identify abrupt changes in the vibration signals. Thirty features were extracted from the
wavelet coefficients of vibration signals using different feature parameters. A data mining technique
was implemented to obtain the superior features and to reduce the dimension of features and the
training time of two classifiers. The accuracy rate of the SVM and ANFIS was calculated by applying
the testing dataset. The experimental results showed that the performance of the SVM and ANFIS was
perfect in fault diagnosis of rolling element bearing. This method allowed identification at a 100.0%
level of efficiency. Therefore, it can be seen that the proposed procedure can be successfully applied
for condition monitoring and fault diagnosis of bearings in industry
شماره مدرك كنفرانس :
4490246
