Fault diagnosis of rolling element bearing using time-domain features and neural networks

Sreejith, B.; Verma, Arun Kumar; Srividya, A.

doi:10.1109/iciinfs.2008.4798444

Cited by 112 publications

(68 citation statements)

References 19 publications

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“…Some features may not contribute to the failure diagnosis and even degrade the performance of the diagnosis and others can have redundant information. For that purpose, a Separation Index is used to define the significance of features [7]. For two signals presented to be compared, let For trajectory prediction purposes, it is important to select features that exhibit some predictable trends that relate to the health of the system.…”

Section: Feature Extractionmentioning

confidence: 99%

Remaining Useful Life Estimation using Time Trajectory Tracking and Support Vector Machines

Galar

Kumar

Lee

et al. 2012

J. Phys.: Conf. Ser.

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Abstract. In this paper, a novel RUL prediction method inspired by feature maps and SVM classifiers is proposed. The historical instances of a system with life-time condition data are used to create a classification by SVM hyper planes. For a test instance of the same system, whose RUL is to be estimated, degradation speed is evaluated by computing the minimal distance defined based on the degradation trajectories, i.e. the approach of the system to the hyper plane that segregates good and bad condition data at different time horizon. Therefore, the final RUL of a specific component can be estimated and global RUL information can then be obtained by aggregating the multiple RUL estimations using a density estimation method.

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Section: Feature Extractionmentioning

confidence: 99%

Remaining Useful Life Estimation using Time Trajectory Tracking and Support Vector Machines

Galar

Kumar

Lee

et al. 2012

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The selection of a proper signal processing technique is important for extracting the fault-related information. The time domain analysis mainly uses the parameters like RMS value, a peak factor, a crest factor, a skewness and kurtosis [14]. Among these, the kurtosis has been found to be very effective.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring of induction motor bearing based on bearing damage index

Patel

Giri

2017

Archives of Electrical Engineering

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Abstract:The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become "transient" in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an 'index' representing the health condition of rotating machines.

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“…Generally, the main feature extraction methods include time-domain methods, frequency-domain methods, and time-frequency methods. Time-domain analysis methods such as root-mean-square (RMS) value, crest factor, form factor, kurtosis and skewness have been successfully used to realize fault diagnosis of rotating machine [4]. Frequency-domain analysis methods include Fourier transform, cepstrum analysis and so on.…”

Section: Introductionmentioning

confidence: 99%

A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis

Sun¹,

Lü²,

Ma³

2017

Vib. proced.

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Abstract.The vibration signals of a gearbox always contain the dynamic operation information, which are important for the feature extraction and further work. However, the low signal-to-noise ratio and combined multi-mode faults make it difficult to extract discriminable features of gearboxes. In this study, a feature fusion method based on wavelet packet decomposition (WPD), singular value decomposition (SVD) and -Distributed stochastic neighbor embedding ( -SNE) for gearbox fault diagnosis is proposed. First, time-frequency analysis method of WPT-SVD as well as time-domain analysis methods are utilized to extract robust feature vectors of gearboxes with different conditions. As an effective method for the visualization of high-dimensional datasets, -SNE is then introduced to realize the dimensionality reduction of feature vectors. Finally, with the fused features, a radial basis function (RBF) neural network is trained to realize the classification of gearbox fault modes. Sufficient experiments have been implemented to validate the effectiveness and superiority of the proposed method by analyzing the vibration signals of gearboxes.

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Fault diagnosis of rolling element bearing using time-domain features and neural networks

Cited by 112 publications

References 19 publications

Remaining Useful Life Estimation using Time Trajectory Tracking and Support Vector Machines

Remaining Useful Life Estimation using Time Trajectory Tracking and Support Vector Machines

Condition monitoring of induction motor bearing based on bearing damage index

A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis

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