Multiscale morphology analysis and its application to fault diagnosis

Zhang, Lijun; Xu, Jun; Yang, Jianhong; Yang, Debin; Wang, Dadong

doi:10.1016/j.ymssp.2007.09.010

Cited by 163 publications

(121 citation statements)

References 13 publications

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“…This is consistent with the idea that multi-scale morphology is used to measure the geometric shape of the object being analyzed at different scales [3]. In order to use the multi-scale morphological operators to compute the fractal dimension, the improvement and optimization of this method from the angle of improving the computational efficiency, using morphological filtering operators of fractal dimension estimation [4][5][6].…”

Section: Introductionsupporting

confidence: 72%

An Investigation on Engine Condition Monitoring Based on EEMD and Morphological Fractal Dimension

Wang¹,

Li²,

Song³

2017

Proceedings of the 2017 2nd International Conference on Automatic Control and Information Engineering (ICACIE 2017)

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Section: Introductionsupporting

confidence: 72%

An Investigation on Engine Condition Monitoring Based on EEMD and Morphological Fractal Dimension

Wang¹,

Li²,

Song³

2017

Proceedings of the 2017 2nd International Conference on Automatic Control and Information Engineering (ICACIE 2017)

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“…Vibration signals under four operational conditions (i.e., 1,797, 1,772, 1,750, 1,730 rpm) were recorded and the sampling frequency is set to 12 kHz with a duration of one second. Bearing datasets of this test stand have been validated for fault diagnosis and performance assessment applications in [44,45]. SKF bearings at the drive end of the motor shaft are studied here.…”

Section: Experiments Descriptionmentioning

confidence: 99%

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

Zhang

et al. 2014

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Performance degradation assessment of rolling element bearings is vital for the reliable and cost-efficient operation and maintenance of rotating machines, especially for the implementation of condition-based maintenance (CBM). For robust degradation assessment of rolling element bearings, uncertainties such as those induced from usage variations or sensor errors must be taken into account. This paper presents an information exergy index for bearing performance degradation assessment that combines singular value decomposition (SVD) and the information exergy method. Information exergy integrates condition monitoring information of multiple instants and multiple sensors, and thus performance degradation assessment uncertainties are reduced and robust degradation assessment results can be obtained using the proposed index. The effectiveness and robustness of the proposed information exergy index are validated through experimental case studies.

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“…Commonly, these faults are often accompanied by nonlinear vibration phenomenon and its vibration signals are often complicated as the strong background noise [1]. Effective analysis method for vibration signal is the focus of much research in mechanical fault monitoring and diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

Luo

2017

Shock and Vibration

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The vibration signal measured from the mechanical equipment is associated with the operation of key structure, such as the rolling bearing and gear. The effective signal processing method for early weak fault has attracted much attention and it is of vital importance in mechanical fault monitoring and diagnosis. The recently proposed atomic sparse decomposition algorithm is performed around overcomplete dictionary instead of the traditional signal analysis method using orthogonal basis operator. This algorithm has been proved to be effective in extracting useful components from complex signal by reducing influence of background noises. In this paper, an improved linear frequency-modulated (Ilfm) function as an atom is employed in the proposed enhanced orthogonal matching pursuit (EOMP) algorithm. Then, quantum genetic algorithm (QGA) with the OMP algorithm is integrated since the QGA can quickly obtain the global optimal solution of multiple parameters for rapidly and accurately extracting fault characteristic information from the vibration signal. The proposed method in this paper is superior to the traditional OMP algorithm in terms of accuracy and reducing the computation time through analyzing the simulation data and real world data. The experimental results based on the application of gear and bearing fault diagnosis indicate that it is more effective than traditional method in extracting fault characteristic information.

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Multiscale morphology analysis and its application to fault diagnosis

Cited by 163 publications

References 13 publications

An Investigation on Engine Condition Monitoring Based on EEMD and Morphological Fractal Dimension

An Investigation on Engine Condition Monitoring Based on EEMD and Morphological Fractal Dimension

Performance Degradation Assessment of Rolling Element Bearings Based on an Index Combining SVD and Information Exergy

Enhanced Orthogonal Matching Pursuit Algorithm and Its Application in Mechanical Equipment Fault Diagnosis

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