Bearing faults classification under various operation modes using time domain features, singular value decomposition, and fuzzy logic system

Abstract

Nowadays, multi-fault diagnosis has become the most interesting topic for researchers, since it has lately attracted a substantial attention. The most published works recently have considered defects detection, identification, and classification as the toughest challenge for rotating machinery monitoring. As feature extraction requires robust techniques for online inspection with a high level of expertise to make automatic decisions on the running machine health status, a robust approach is required to adjust the misclassification of the extracted features, especially under various working conditions. In this paper, we propose the combination of two Time Domain Features (TDFs) in tandem with Singular Value Decomposition (SVD) and Fuzzy Logic System (FLS) to build an enhanced fault diagnosis technique for rolling bearing. The original vibration signal is divided first into several data samples. Thereafter, TDFs are applied on each sample to construct a feature matrix during the feature extraction step. Afterwards, SVD is performed on the obtained matrices in order to reduce their dimension and select the most stable vectors (singular values). Finally, FLS is employed as a powerful tool for automatic feature classification. Experimental results confirm that our suggested approach can enhance the ability to assess the degradation of bearing faults with a higher recognition sensitivity even under different working conditions.

Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications

“…Shannon entropy has been presented as a method for fault health monitoring. Morlet wavelet was found the best fit to the impulse response of fault in the vibration signal [ 46 , 47 ] out of DB2, morlet, and meyer wavelets in the high frequency zone. Measurement of defect width and SEM analysis were used to evaluate the proposed approach.…”

Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal

“…Currently, intelligent classification techniques have gained popularity in numerous industrial applications. [23][24][25][26][27] Fuzzy logic (FL) and Artificial Neural Network (ANN) have been effectively used for gear and bearing diagnosis. Fuzzy and neural techniques are soft computing approaches that can be easily implemented in complex systems to monitor their condition.…”

Air compressor fault classification using MODWPT, time domain features, WSA and machine learning classifiers based on acoustic analysis

“…Usually, vibration analysis is frequently regarded and used since vibration signals are not intrusive in the operation of machinery [1]. For instance, several works [2][3][4][5][6][7][8][9][10][11] involve gear and bearing faults detection, identification, and classification in speed reducers and wind turbines are based on vibration data analysis. However, for air compressor condition monitoring, acoustic recordings and acquisition using acoustic sensors appear to be more advantageous and reliable than vibration monitoring as given in [12][13][14].…”