A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fault Detection of Rolling Element Bearings using Advanced Signal Processing Technique
In rotating machinery, rolling element bearings are one of the most critical components and a large majority of system failures arise from faulty bearings. Hence, there is an increasing demand to find an effective and reliable condition monitoring technique. In this paper, a procedure for detecting various types of bearing faults using thermal imaging is presented and assessed. Five different fault cases are tested: No Fault (NF), Line Fault (LF), Small Circle Fault (SCF), Double Line Fault (DLF), and Large Circle Fault (LCF). Experiments were conducted on the BENTLY NEVADA RK4 Rotor Kit. A novel signal processing algorithm is proposed to detect the faults which involves utilizing the Discrete Wavelet Transform (DWT). The HAAR wavelet is used as the mother wavelet and a decomposition level of 7 is used. The inverse of HAAR is applied on the decomposed signal and the envelop spectrum is plotted. A classifier is then created to identify the fault that utilizes the Support Vector Machine (SVM) classifier to extract the features and a confusion matrix is developed to detect the prediction accuracy.
Fault Detection of Rolling Element Bearings using Advanced Signal Processing Technique
In rotating machinery, rolling element bearings are one of the most critical components and a large majority of system failures arise from faulty bearings. Hence, there is an increasing demand to find an effective and reliable condition monitoring technique. In this paper, a procedure for detecting various types of bearing faults using thermal imaging is presented and assessed. Five different fault cases are tested: No Fault (NF), Line Fault (LF), Small Circle Fault (SCF), Double Line Fault (DLF), and Large Circle Fault (LCF). Experiments were conducted on the BENTLY NEVADA RK4 Rotor Kit. A novel signal processing algorithm is proposed to detect the faults which involves utilizing the Discrete Wavelet Transform (DWT). The HAAR wavelet is used as the mother wavelet and a decomposition level of 7 is used. The inverse of HAAR is applied on the decomposed signal and the envelop spectrum is plotted. A classifier is then created to identify the fault that utilizes the Support Vector Machine (SVM) classifier to extract the features and a confusion matrix is developed to detect the prediction accuracy.
Fault Detection of Rolling Element Bearings using Advanced Signal Processing Technique
Azeez, Abid Abdul (author) / Alkhedher, Mohammad (author) / Gadala, Mohamed S. (author) / Mohamad, Omar Ahmad (author)
2020-02-01
799104 byte
Conference paper
Electronic Resource
English
FAULT FEATURE EXTRACTION OF ROLLING ELEMENT BEARINGS BASED ON ADAPTIVE MCKD
| DOAJ | 2020
Failure Modes of Rolling Element Bearings
| British Library Conference Proceedings | 1994
| British Library Online Contents | 2007
Fault Diagnosis of Rolling Bearings Based on LLE_KFDA
| British Library Online Contents | 2009