Multi-Fractal Based Fault Diagnosis Method of Rotating Machinery

Zhang, Shu Qing; He, Yu; Zhang, Jin Min; Zhao, Yu Chun

doi:10.4028/www.scientific.net/amm.130-134.571

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…In a simple prototype, signals are placed within the grids, and the concept of weight is introduced. The average probability in each grid is denoted as α, which is referred to as Holder exponent [27,28]. Based on the definition of the fractal dimensions, N = r D , can be changed to p i (ϵ) = ϵ ϵ i .…”

Section: Definition Of Fractal Dimensionsmentioning

confidence: 99%

A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis

Tang,

2023

Mach. Learn.: Sci. Technol.

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Bearings are one of the critical components of any mechanical equipment. They induce most equipment faults, and their health status directly impacts the overall performance of equipment. Therefore, effective bearing fault diagnosis is essential, as it helps maintain the equipment stability, increasing economic benefits through timely maintenance. Currently, most studies focus on extracting fault features, with limited attention to establishing fault thresholds. As a result, these thresholds are challenging to utilize in the automatic monitoring diagnosis of intelligent devices. This study employed the generalized fractal dimensions to effectively extract the feature of time-domain vibration signals of bearings. The optimal fault threshold model was developed using the receiver operating characteristic curve, which served as the baseline of exception judgment. The extracted fault threshold model was verified using two bearing operation experiments. The experimental results revealed different damaged positions and components observed in the two experiments. The same fault threshold model was obtained using the method proposed in this study, and it effectively diagnosed the abnormal states within the signals. This finding confirms the effectiveness of the diagnostic method proposed in this study.

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Section: Definition Of Fractal Dimensionsmentioning

confidence: 99%

A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis

Tang,

2023

Mach. Learn.: Sci. Technol.

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“…For = 0, it is a volumetric dimension (box type), for → 1 an information dimension (in accordance with the Shannon entropy definition), while for = 2 -a correlation dimension expressing the probability of finding a pair of points of the phase attractor -reconstructed on the time series basis -in a distance smaller than the determined one. The mentioned connection of the multifractal spectrum with the Renyi dimension allows to assess the time series on the basis of the algorithm of Multifractal Detrended Fluctuations Analysis (MF-DFA) [16,17], which leads to determining the generalised Hurst exponent ( ) [18,19], from the dependence describing the signal fluctuations scaling:…”

Section: Fig 1 Segmentation Of the Test Signalmentioning

confidence: 99%

A generalised entropy in multifractal time signals analysis of mechanical vibration

Puchalski¹,

Komorska²

2018

J. vibroeng.

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The entropy theory finds wider and wider applications in the description of real time series recorded in complex systems. The relation of the multifractal spectrum with the Renyi dimension allows to assess the generalised entropy of time series based on the multifractal formalism. The aim of the hereby paper is to present the form and properties of entropy, which can be used in defining measures in the comparative analysis of mechanical vibrations time series being the main information source on the complex systems states. The obtained results were verified for signals recorded in various maintenance states of the drive system of the spark ignition vehicle, being the effect of active experiment with implemented typical mechanical defects.

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“…It is a new trend in solving practical problems involving the study of short-term signals in a transient process with strong nonlinearity, based on the fractal theory in order to diagnose damage to mechanical equipment. The impact of damage to the outer race of a rolling bearing on the width of the multifractal spectrum is presented in [30]. Detrended fluctuation analysis MF-DFA is a commonly used algorithm for multifractal analysis.…”

Section: Introductionmentioning

confidence: 99%

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Komorska

Puchalski

2021

Sensors

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Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.

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Multi-Fractal Based Fault Diagnosis Method of Rotating Machinery

Cited by 5 publications

References 2 publications

A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis

A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis

A generalised entropy in multifractal time signals analysis of mechanical vibration

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

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