Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals

Immovilli, Fabio; Cocconcelli, Marco; Bellini, A.; Rubini, Riccardo

doi:10.1109/tie.2009.2025288

Cited by 171 publications

(50 citation statements)

References 26 publications

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“…The characteristic frequencies of these vibrations rely on fault location, bearing geometry, and operating speed [20,21]. Not alike localized faults, generalized-roughness faults greatly worsen the entire area of a bearing element because of the absence of lubrication, erosion, or bearing pollution and are hard to represent by specific frequencies [22,23]. In this paper, only localized fault is studied.…”

Section: The Structure and Fault Types Of A Rolling-element Bearingmentioning

confidence: 99%

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Song¹,

Hu²,

Zhu³

et al. 2017

Mathematical Problems in Engineering

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Bearing faults are the most frequent faults of induction motors. The current spectrum analysis is an easy and popular method for the monitoring and detection of bearing faults. After a presentation of the existing fault models, this paper illustrates an analytical approach to evaluate the effects of the slot harmonics on the stator current in an induction motor with bearing fault. Simple and clear theoretical analysis results in some new current characteristic frequencies. Experimental tests with artificial bearing outer raceway fault are carried out by measuring stator current signals. The experimental results by spectral analysis of the stator current agree well with the theoretical inference.

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Section: The Structure and Fault Types Of A Rolling-element Bearingmentioning

confidence: 99%

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Song¹,

Hu²,

Zhu³

et al. 2017

Mathematical Problems in Engineering

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“…SK was firstly presented by Dwyer in 1983 [26]- [28], and several studies have shown that SK can be successfully used to identify transient impacts caused by faults of rolling bearings [29]- [30]. The SK is the fourth-order cumulant of each frequency component of a signal.…”

Section: The Spectral Kurtosis (Sk) Of Vibration Signalsmentioning

confidence: 99%

A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

Huang¹,

Ouyang²,

Gao

et al. 2016

Measurement Science Review

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Detection of incipient degradation demands extracting sensitive features accurately when signal-to-noise ratio (SNR) is very poor, which appears in most industrial environments. Vibration signals of rolling bearings are widely used for bearing fault diagnosis. In this paper, we propose a feature extraction method that combines Blind Source Separation (BSS) and Spectral Kurtosis (SK) to separate independent noise sources. Normal, and incipient fault signals from vibration tests of rolling bearings are processed. We studied 16 groups of vibration signals (which all display an increase in kurtosis) of incipient degradation after they are processed by a BSS filter. Compared with conventional kurtosis, theoretical studies of SK trends show that the SK levels vary with frequencies and some experimental studies show that SK trends of measured vibration signals of bearings vary with the amount and level of impulses in both vibration and noise signals due to bearing faults. It is found that the peak values of SK increase when vibration signals of incipient faults are processed by a BSS filter. This pre-processing by a BSS filter makes SK more sensitive to impulses caused by performance degradation of bearings.

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“…According to Tables 1 and 2, the total energy growth rate of the rolling bearing is sensitive to the running conditions. Thus, fault diagnosis of the rolling bearing during run-up based on the total energy growth rate is effective and superior to those with the EMD energy entropy [16], spectral kurtosis energy [17], and first left and right singular vectors of time-frequency spectrum [18] in computational efficiency and accuracy.…”

Section: Fault Diagnosismentioning

confidence: 99%

Total Energy Growth Rate of Rolling Bearing due to Faults during Run-Up

Wang

Zhu

et al. 2013

Advances in Mechanical Engineering

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In order to improve the reliability of fault diagnosis, total energy growth rate of rolling bearing during run-up is defined and applied to bearing detection. Firstly, an approach of merging short-time Fourier transform (STFT), linear fitting and median filtering is developed to extract the total energy growth rate. Secondly, the relationship between the total energy growth rate and different running conditions is discussed. Thirdly, the total energy growth rate is adopted to diagnose faults as input vector of radial basis function (RBF) neural network. Experiment results show that the total energy growth rate is an effective failure symbol for fault diagnosis of rolling bearing during run-up.

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Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals

Cited by 171 publications

References 26 publications

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

Effects of the Slot Harmonics on the Stator Current in an Induction Motor with Bearing Fault

A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

Total Energy Growth Rate of Rolling Bearing due to Faults during Run-Up

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