Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum

Al-Raheem, Khalid F.; Roy, Asok; Ramachandran, K. P.; Harrison, David K.; Grainger, Sally

doi:10.1155/2007/73629

Cited by 26 publications

(17 citation statements)

References 23 publications

(27 reference statements)

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“…where 1 and 2 are the weight coefficients of the smoothness index and the kurtosis, respectively. SIðx 1 Þ is the smoothness index 29,30 of the high resonance component, and Kurðx 2 Þ is the kurtosis 31,32 of the low resonance component. When there is a localized fault on the bearing or the gear in the rotating machine, it will produce a series of transient impulses which have low oscillatory behavior.…”

Section: Separation Of the High Resonance Component And The Low Resonmentioning

confidence: 99%

Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery

Zhang

Xing

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction. In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration signals of rotating machinery into the high and the low resonance components. To extract the weak fault characteristic signals in the presence of strong noise effectively, the genetic algorithm is used to obtain the optimal decomposition parameters. Then, the optimal high and low resonance components, which include the fault characteristic signals of rotating machinery, can be obtained by using the resonance-based signal spares decomposition method with the optimal decomposition parameters. Finally, the high and the low resonance components are subject to the Hilbert transform demodulation analysis; the faults of rotating machinery can be diagnosed based on the obtained envelop spectra. The optimal resonance-based signal spares decomposition method is successfully applied to the analysis of the simulation and experiment vibration signals. The analysis results demonstrate that the proposed method can successfully extract the fault features in rotating machinery.

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Section: Separation Of the High Resonance Component And The Low Resonmentioning

confidence: 99%

Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery

Zhang

Xing

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Bearings are critical components in rotating machines because their failure could lead to serious damage in machines. In recent years, bearing fault diagnosis has received increasing attention [34][35][36]. In this case, vibration signals are acquired from the ball bearing housing for performance assessment and fault diagnosis.…”

Section: Performance Assessment and Fault Diagnosis For Ball Bearingmentioning

confidence: 99%

An approach to performance assessment and fault diagnosis for rotating machinery equipment

Tao

Chuan

et al. 2013

EURASIP J. Adv. Signal Process.

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Predict and prevent maintenance is routinely carried out. However, how to address the problem of performance assessment maximizing the use of available monitoring data, and how to build a framework that integrates performance assessment, fault detection, and diagnosis are still a significant challenge. For this purpose, this article introduces an approach to performance assessment and fault diagnosis for rotating machinery, including wavelet packet decomposition for extracting energy feature samples from vibration signals acquired during normal and faulty conditions; clustering analysis for demonstrating the separability of the samples; and Fisher discriminant analysis for providing an optimal lower-dimensional representation, in terms of maximizing the separability among different populations, by projecting the samples into a new space. In the new low-dimensional space, the Mahalanobis distance (MD) between the new measurement data and normal population can be calculated for performance assessment. Moreover, this model for performance assessment only requires data to be available in normal conditions and any one of all possible fault conditions, without the necessity for the full life cycle of condition monitoring data. In addition, if monitoring data under different fault conditions are available, the fault mode can be identified accurately by comparing the MDs between the new measurement data and each fault population. Finally, the proposed method was verified to be successful on performance assessment and fault diagnosis via a hydraulic pump test and a ball bearing test.

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“…Therefore, if a wavelet function with certain shape which is close to an impulse is chosen, the noise could be effectively suppressed. 30 Laplace wavelet has similar shape with impulses, and has been widely used in impulse detection. 31 In the next section, the well-established wavelet transform theory and Laplace wavelet filter are briefly introduced.…”

Section: Characteristics Of Vibration and Friction Torque Signal Of Smentioning

confidence: 99%

Performance degradation analysis of solid lubricated bearings based on Laplace wavelet filter and self-organizing map

Zhang

Wang

2013

Proceedings of the Institution of Mechanical Engineers, Part C:

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Solid lubricated bearings are commonly used in space mechanisms and other appliances, and their reliability analysis has drawn more and more attention. This paper focuses on the performance degradation analysis of solid lubricated bearings. Based on the vibration and friction torque signal of solid lubricated bearings, Laplace wavelet filter is adopted to process vibration signal and feature vector is constructed by calculating time-domain parameters of filtered vibration signal and original friction torque signal. Self-organizing map is then adopted to analyze the performance degradation based on extracted feature vectors. Experimental results show that this method can describe performance degradation process effectively.

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Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum

Cited by 26 publications

References 23 publications

Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery

Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery

An approach to performance assessment and fault diagnosis for rotating machinery equipment

Performance degradation analysis of solid lubricated bearings based on Laplace wavelet filter and self-organizing map

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