Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Frosini, Lucia; Harlişca, Ciprian; Szabó, Loránd

doi:10.1109/tie.2014.2361115

Cited by 230 publications

(136 citation statements)

References 40 publications

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“…Step (2) predicts the trends of the selected features and estimates the RUL of the slew bearing. In this step, kernel regression is used to predict the trend of the time-domain kurtosis, WD kurtosis and the LLE features.…”

Section: Integrated Condition Monitoring and Prognosis Methods For Lowmentioning

confidence: 99%

“…T is further divided into memory matrix D and remaining training matrix L. It should be noted that T holds data from the normal state while P obs holds the monitored state, as shown in Equation (2).…”

Section: Yesmentioning

confidence: 99%

“…The initial threshold is set at four 4 times the kurtosis value of the normal bearing condition. In Step (2), the data point that exceeds the predetermined initial threshold of each selected feature is saved and used for prediction. From the laboratory slew bearing data, it was found that the minimum number of data points used for prediction is 3.…”

Section: Step (2) Prediction Of the Future State Of Monitored Parametmentioning

confidence: 99%

See 2 more Smart Citations

Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings

2017

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This paper presents an application of multivariate state estimation technique (MSET), sequential probability ratio test (SPRT) and kernel regression for low speed slew bearing condition monitoring and prognosis. The method is applied in two steps.Step (1) is the detection of the incipient slew bearing defect. In this step, combined MSET and SPRT is used with circular-domain kurtosis, time-domain kurtosis, wavelet decomposition (WD) kurtosis, empirical mode decomposition (EMD) kurtosis and the largest Lyapunov exponent (LLE) feature.Step (2) is the prediction of the selected features' trends and the estimation of the remaining useful life (RUL) of the slew bearing. In this step, kernel regression is used with time-domain kurtosis, WD kurtosis and the LLE feature. The application of the method is demonstrated with laboratory slew bearing acceleration data.

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Section: Integrated Condition Monitoring and Prognosis Methods For Lowmentioning

confidence: 99%

Section: Yesmentioning

confidence: 99%

Section: Step (2) Prediction Of the Future State Of Monitored Parametmentioning

confidence: 99%

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Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings

2017

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“…The time-frequency analysis method has been widely used in faults diagnosis, because it can provide the information in the time and frequency domain [2]. Moreover, there are many methods of artificial intelligence detection, such as statistical processing to sense [3], stray magnetic flux measurement [4], and neural networks such as support vector machine (SVM) [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Gao

Villecco

et al. 2017

Entropy

140

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Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

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“…Meanwhile, various nonlinear multi-body dynamic models have been proposed, due to Hertzian contact, unbalanced rotor effects, radial internal clearance, raceway spalls and the size of the rolling elements [4][5][6][7]. Diagnosis analyses cover the exploration and development of approaches for the feature extraction and identification for rolling bearing faults from the viewpoint of signal processing or information theory, such as statistical processing, fractal dimension, linear discriminant analysis, cepstrum analysis, time-frequency analysis, supervised learned processing and so on [8][9][10][11][12][13][14][15][16][17][18][19][20]. Few studies however have investigated and extracted the characteristics of the vibration signals on rolling bearings in terms of dynamics theory.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method

Lei

Meng

Dong

2017

Entropy

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Abstract:Bearing vibration response studies are crucial for the condition monitoring of bearings and the quality inspection of rotating machinery systems. However, it is still very difficult to diagnose bearing faults, especially rolling element faults, due to the complex, high-dimensional and nonlinear characteristics of vibration signals as well as the strong background noise. A novel nonlinear analysis method-the symplectic entropy (SymEn) measure-is proposed to analyze the measured signals for fault monitoring of rolling bearings. The core technique of the SymEn approach is the entropy analysis based on the symplectic principal components. The dynamical characteristics of the rolling bearing data are analyzed using the SymEn method. Unlike other techniques consisting of high-dimensional features in the time-domain, frequency-domain and the empirical mode decomposition (EMD)/wavelet-domain, the SymEn approach constructs low-dimensional (i.e., two-dimensional) features based on the SymEn estimate. The vibration signals from our experiments and the Case Western Reserve University Bearing Data Center are applied to verify the effectiveness of the proposed method. Meanwhile, it is found that faulty bearings have a great influence on the other normal bearings. To sum up, the results indicate that the proposed method can be used to detect rolling bearing faults.

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Induction Machine Bearing Fault Detection by Means of Statistical Processing of the Stray Flux Measurement

Cited by 230 publications

References 40 publications

Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings

Integrated Condition Monitoring and Prognosis Method for Incipient Defect Detection and Remaining Life Prediction of Low Speed Slew Bearings

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Fault Detection for Vibration Signals on Rolling Bearings Based on the Symplectic Entropy Method

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