Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal

Nayana, B. R.; Geethanjali, P.

doi:10.1109/jsen.2017.2727638

Cited by 167 publications

(70 citation statements)

References 28 publications

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“…Tabrizi and team presented a method using WPD, Ensemble empirical mode decomposition (EEMD) and SVM to detect rolling element bearing defect with 93.8% accuracy [15]. In recent articles, Multi scale permutation entropy (MPE) of WPT feature, time domain based methods, Fourier Bessel expansion was used to extract features for successful classification of bearing defects with classification accuracy 94.2%, 99.89%, 98.1%, 98.94% and 96.33% [16,17,19,20,24]. In [29], Fourier-Bessel (FB) expansion and simplified Fuzzy ARTMAP (SFAM) has been used to derive bearing health condition with 100% accuracy using stator current but it suffers at high frequency of signal.…”

Section: Discussion About Resultsmentioning

confidence: 99%

“…In this study, logarithmic root mean square features (LRMSF) of decomposed vibration signal have been used to detect inner race and ball bearing defects due to its capability to reduce the nonlinearity problems of signal. The performance of any classification technique depends on the selection of appropriate number of features and discriminating capability of features [19,20,21]. For this, Fisher's ranking method [22] was employed to select top ten features out of thirty one WPT features extracted from vibration signal.…”

Section: Introductionmentioning

confidence: 99%

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Bearing Fault Detection Using Logarithmic Wavelet Packet Transform and Support Vector Machine

Yadav¹,

Pahuja²

2019

IJIGSP

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This paper presents an automated approach that combines Fisher ranking and dimensional reduction method as kernel principal component analysis (KPCA) with support vector machine (SVM) to accurately classify the defects of rolling element bearing used in induction motor. Methodology: In this perspective, vibration signal produced by rolling element bearing was decomposed to four levels using wavelet packet decomposition (WPD) method. Thirty one Logarithmic Root Mean Square Features (LRMSF) were extracted from four level decomposed vibration signals. Initially, thirty one features were rank by Fisher score and top ten rank features were selected. For effective detection, top ten features were reduced to a new feature using dimension reduction methods as KPCA and generalized discriminant analysis (GDA). After this, the new feature applied to SVM for binary classification of bearing defects. For analysis of this thirty six standard vibration datasets taken from online available bearing data center website of Case Western Reserve University on bearing conditions like healthy (NF), inner race defect (IR) and ball bearing (BB) defects at different loads. Results: The simulated numerical results show that proposed method KPCA with SVM classifier using Gaussian Kernel achieved an accuracy (AC) of 100, Sensitivity (SE) of 100%, Specificity (SP) of 99.3% and Positive prediction value (PPV) of 99.3% for NF_IRB dataset, and an AC of 100, SE of 99.8%, SP of 100% and PPV of 100% for NF_BBB dataset.

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Section: Discussion About Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bearing Fault Detection Using Logarithmic Wavelet Packet Transform and Support Vector Machine

Yadav¹,

Pahuja²

2019

IJIGSP

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“…The traditional rolling-element bearing fault diagnosis mainly adopts signal processing and machine learning techniques. The vibration signal processing techniques used in rolling-element bearing fault diagnosis mainly include time-domain analysis [2], frequency-domain analysis [3] and time-frequency analysis [4][5][6][7]. The wavelet analysis [4], short-time Fourier transform (STFT) [5], empirical mode decomposition [6] and singular value decomposition [7] are commonly used methods in time-frequency analysis of vibration signals of rolling-element bearing.…”

Section: Introductionmentioning

confidence: 99%

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

Wan

Chen

et al. 2020

Sensors

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To address the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional LeNet-5 network used in rolling-element bearing fault diagnosis, a rolling-element bearing fault diagnosis method using improved 2D LeNet-5 network is put forward. The following improvements to the traditional LeNet-5 network are made: the convolution and pooling layers are reasonably designed and the size and number of convolution kernels are carefully adjusted to improve fault classification capability; the batch normalization (BN) is adopted after each convolution layer to improve convergence speed; the dropout operation is performed after each full-connection layer except the last layer to enhance generalization ability. To further improve the efficiency and effectiveness of fault diagnosis, on the basis of improved 2D LeNet-5 network, an end-to-end rolling-element bearing fault diagnosis method based on the improved 1D LeNet-5 network is proposed, which can directly perform 1D convolution and pooling operations on raw vibration signals without any preprocessing. The results show that the improved 2D LeNet-5 network and improved 1D LeNet-5 network achieve a significant performance improvement than traditional LeNet-5 network, the improved 1D LeNet-5 network provides a higher fault diagnosis accuracy with a less training time in most cases, and the improved 2D LeNet-5 network performs better than improved 1D LeNet-5 network under small training samples and strong noise environment.Sensors 2020, 20, 1693 2 of 23 clustering [10], back propagation neural network (BPNN) [11], etc. The traditional rolling-element bearing fault diagnosis methods have been widely used, but with the increasing complexity of vibration signals, these methods have a certain limitation; however, the deep learning methods have a greater advantage in analyzing complicated and non-stationary vibration signals.The deep learning methods can automatically extract fault features from vibration signals [12], recently there are many researches are conducted on rolling-element bearing fault diagnosis using deep learning. Yin et al. [13] extracted the original features of vibration signals through time-domain analysis, frequency-domain analysis and wavelet transform, and obtained the low-dimensional features from 38 original features using the nonlinear global algorithm, and the low-dimensional features array is input into the deep belief network (DBN) to evaluate the performance status of rolling-element bearing. Liu et al. [14] obtained the spectrogram of vibration signals through STFT, used the stacked sparse auto-encoder (SAE) to automatically extract fault features, and employed the softmax regression to identify the fault type of rolling-element bearing. Liu et al. [15] used the recurrent neural network (RNN) to classify the faults of rolling-element bearing, and adopted the gated recurrent unit based denoising auto-encoder to enhance fault classification accuracy. Among different deep learning methods, compared with DBN, ...

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“…The unit displays three typical target signals respectively, namely individual walking, car moving, and manual drilling. In [5], researchers provide an analysis of the effectiveness of statistical time domain features in determining the vibration signal error. The authors attempted to use the time domain feature to determine the characteristics of mechanical failure of the engine induction.…”

Section: Introductionmentioning

confidence: 99%

PCA Classification of vibration signals in WSN based oil pipeline monitoring system

Shareef

Mossa

2019

J. Al-Qadisiyah Comp. Sci. Math.

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Using wireless sensor network technology in structure health monitoring applications results in generating large amount of data. To sift through this data and extract useful information an extensive data analysis should be applied. In this paper, a Wireless Sensor Network (WSNs) is proposed for the oil pipeline monitoring system with proposed method for event detection and classification. The method depends on the Principal Component Analysis (PCA). It applied to features extracted from vibration signals of the monitored pipeline. These vibration signals are collected while applying damage events (knocking and drilling) to the oil pipeline. PCA is applied to features extracted from both time domain and frequency domain. The results manifest that this method is able to detect the existence of damage and also to distinguish between the different levels of harmful events applied to the pipeline.

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Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal

Cited by 167 publications

References 28 publications

Bearing Fault Detection Using Logarithmic Wavelet Packet Transform and Support Vector Machine

Bearing Fault Detection Using Logarithmic Wavelet Packet Transform and Support Vector Machine

Rolling-Element Bearing Fault Diagnosis Using Improved LeNet-5 Network

PCA Classification of vibration signals in WSN based oil pipeline monitoring system

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