Gearbox fault diagnosis using RMS based probability density function and entropy measures for fluctuating speed conditions

Sharma, Vikas; Parey, Anand

doi:10.1177/1475921716679802

Cited by 30 publications

(20 citation statements)

References 33 publications

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“…The procedure leads to the definition of an entropy indicator, BE, which is a parameter related to the SE-Energy (H-E) curve, where H and E are the SE and the energy of the signals, respectively. [2], has been widely applied in mechanical engineering [4], as well as in clinical and biological studies [5], where there is the need to analyze pathological behaviors of complex phenomena (e.g., heartbeat). SE use the Time-Frequency distribution (TFD) of a signal x(t) to build a probabilistic distribution, P(q), as follows:…”

Section: Introductionmentioning

confidence: 99%

Use of Spectral Entropy for Damage Detection in Masonry Buildings in the Presence of Mild Seismicity

Ceravolo

Lenticchia

Miraglia

2018

The 18th International Conference on Experimental Mechanics

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Abstract:The seismic events that struck central Italy in 2016 caused severe damage to a wide range of buildings and infrastructures. Masonry buildings were particularly affected, even for low values of peak ground acceleration. In this paper, the data recorded for three masonry buildings belonging to the Seismic Observatory of Structures (OSS) network are used to detect their seismic damage by means of Spectral Entropy (SE). However, entropy measures are sensitive to the energy inserted in the system, since an input of energy can lead to a more deterministic behavior of the structure and thus to a reduction of the entropy indicator. When non-stationary time series are used to evaluate the presence of damage (e.g. mild seismicity) the entropy of the system could be underestimated, leading to misleading results. For these reasons, in this paper an indicator based on SE is proposed to assess the occurrence of damage also in the presence of mild seismicity.

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Section: Introductionmentioning

confidence: 99%

Use of Spectral Entropy for Damage Detection in Masonry Buildings in the Presence of Mild Seismicity

Ceravolo

Lenticchia

Miraglia

2018

The 18th International Conference on Experimental Mechanics

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“…Compared with vibration interval, transmission reliability of the gear system is the most obvious performance index. Herein, the distribution of allowable vibration velocity is set as a normal distribution (Sharma and Parey, 2017), and the mean of distribution is set as 2.5 times the root meam square of vibration velocity in normal operation according to the law shown in ISO2372. Therefore, the distribution of allowable vibration velocity is set as N 2 ð120; 8 2 Þ mm/s.…”

Section: Effects Of Interval Deviation On Transmission Reliabilitymentioning

confidence: 99%

Effects of random interval parameters on spur gear vibration

Jian

Zhang

2020

Journal of Vibration and Control

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Gear driving devices are commonly used in mechanical transmission systems. Due to the inevitable random errors in manufacturing, installation, and operation, the dynamics of a gear transmission system will fluctuate randomly. To reveal the dynamic characteristics, an interval parameters dynamic model of a single-stage spur gear pair is established, in which the uncertainties of displacement excitation, load excitation, and stiffness excitation are included, and their formulations are derived in detail by using interval mathematics. The established interval parameters dynamic model is solved by combining the Chebyshev inclusion function method and the Runge–Kutta method. Finally, the influence of the random interval parameters of meshing stiffness, input torque, and transmission error as well as backlash on the vibration velocity interval and transmission reliability of the gear transmission system are studied. The analysis results show that the three types of excitations have different effects on the dynamic characteristics. To be specific, the effects of uncertain parameters on the dynamic characteristics can be ordered as meshing stiffness, input torque, backlash, and transmission error in sequence from the strongest to the weakest. The present study may serve as a sound theoretical basis and can provide references for the design and vibration control of spur gear transmission systems.

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“…A denoising method based on VMD and approximate entropy was developed by An and Yang 24 and demonstrated its performance by comparing it with wavelet transform. It has been noted that sample entropy is better than approximate entropy, 25 and permutation entropy (PE) performs better than sample entropy 26 toward uncertainty and randomness. PE has high sensitivity to signal change, strong noise cancelation ability, and fast running speed, as proposed by Bandt and Pompe, 27 to detect the randomness of time series 28 .…”

Section: Introductionmentioning

confidence: 99%

“…The smaller value of entropy implies the lower uncertainty present in signals and vice versa. Thus, the entropy will rise with increasing fault, owing to better predictability of faults 25,29 . Thus, PE can be used to differentiate the gear faults.…”

Section: Introductionmentioning

confidence: 99%

Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

Sharma

2020

Wind Energy

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Structural compactness with multiple meshing points excites multiple vibrations in a planetary gearbox of a wind turbine operating at varying speeds. The nonstationary, multicomponent vibration signals result in complex modulations challenging the effectiveness of a signal processing technique‐based fault diagnosis method. This paper aims to detect gear tooth faults under varying speed conditions based on the instantaneous frequency (IF) estimate of the decomposed component. Vibration signals were decomposed using variational mode decomposition (VMD), which is capable of frequency demodulation by exhibiting mono‐oscillatory components, followed by the IF estimation to select the most sensitive decomposed mode. The selected sensitive mode was further analyzed to deduce envelope spectrums for the extraction of frequency components highlighting fault symptoms. Furthermore, permutation entropy (PE) was evaluated to characterize the presence of randomness due to fault severity and to validate the proposed signal processing approach. In this paper, the vibration signals simulating planet gear faults were analyzed using the proposed signal processing approach. Symptoms of planet tooth faults were analyzed using the experimental investigation under real‐time varying speed to validate and exhibit the effectiveness of the proposed fault diagnosis approach for a planetary gearbox.

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Gearbox fault diagnosis using RMS based probability density function and entropy measures for fluctuating speed conditions

Cited by 30 publications

References 33 publications

Use of Spectral Entropy for Damage Detection in Masonry Buildings in the Presence of Mild Seismicity

Use of Spectral Entropy for Damage Detection in Masonry Buildings in the Presence of Mild Seismicity

Effects of random interval parameters on spur gear vibration

Gear fault detection based on instantaneous frequency estimation using variational mode decomposition and permutation entropy under real speed scenarios

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