Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

Abstract

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.

An adaptive multiple time domain synchronous averaging method and its application in vibration signal feature enhancement

“…In order to verify the superiority of the proposed method, some classical methods and the latest proposed methods are compared with aMTSA. NRC [16], the method in [12], autocorrelation denoising, empirical wavelet decomposition [26], minimum entropy deconvolution [27], maximum correlation kurtosis deconvolution [28], and multi-point optimal minimum entropy deconvolution adjustment [29] are compared with aMTSA. To facilitate expression, these methods are expressed as M1, M2, M3, M4, M5, M6, M7, and aMTSA as M8.…”

“…To solve this problem, one kind of method used is to combine decomposition technology [8,9] and the timefrequency method with TSA. The original signal is decomposed by empirical mode decomposition [10], variational modal decomposition, singular value decomposition (SVD) [11,12] and other technologies, then the mode with obvious features is selected, and the average signal with enhanced features is obtained by TSA technology. The target of these methods is noisy signal, and there are many achievements in weak feature detection.…”

Computer numerical control machine tool wear monitoring through a data-driven approach

“…[1][2][3][4] Degradation in dynamic systems state is a completely normal (inevitable) phenomenon, since such systems operate constantly under severe conditions and in continuous (repetitive) tasks. 5,6 Therefore, a fault presence is a matter of component operating time. The objective in this domain is to monitor the system's condition (to detect and diagnose any occurring defects) and, more importantly, to track the fault's evolution for an accurate estimation of the component's future 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].…”