Frequency domain averaging based experimental evaluation of gear fault without tachometer for fluctuating speed conditions

Sharma, Vikas; Parey, Anand

doi:10.1016/j.ymssp.2016.08.015

Cited by 41 publications

(14 citation statements)

References 25 publications

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“…In this section, the results of the experimental investigation of the proposed gear fault detection method considering varying speed conditions are illustrated and discussed. Such speed variations have not been reported in various prior research work 4,8,22,32 but well observed in real‐life applications 1 . The performance of the proposed fault detection approach based on IF estimation was examined using the vibration signals recorded from a planetary gearbox test‐bed under nonstationary speed conditions.…”

Section: Demonstration Study Using Experimental Datamentioning

confidence: 74%

“…However, issues like mode mixing caused by overenvelopes, underenvelopes, and proof of mathematical foundation arise in the EMD method, thereby limiting its performance. Researchers such as Deng et al, 5 Zhong and Shixiong, 6 Parey and Pachori, 7 and Sharma and Parey 8 suggested corrections for EMD to address its mode mixing problem. Ensemble EMD (EEMD) improvises mode mixing in a limited way, but the selection of white noise is again a challenge.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Demonstration Study Using Experimental Datamentioning

confidence: 74%

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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“…When gears break down, the faulty component often causes an impact, which generates impact energy. Types of gear fault include missing tooth, broken tooth, chipping tip, root crack, and spalling conditions [36][37][38]. However, in the case of a broken tooth, the gearbox needs to change to a new gear.…”

Section: Verification and Discussion Of The Proposed Methodsmentioning

confidence: 99%

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Wang

Lee

2019

Applied Sciences

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Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

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“…El monitoreo de vibraciones ha demostrado ser una técnica eficiente en la detección de fallos en maquinaria rotativa y en los sistemas mecánicos empleados en diferentes sectores de la industria (V. Sharma & Parey, 2017). En el MC el análisis de vibraciones es la técnica más utilizada de la industria, debido a que puede identificar con gran precisión el 90% de todos los fallos de maquinaria por el cambio en las señales de vibración que se producen y el nivel de señal puede dar una predicción con gran exactitud de un fallo futuro (Devendiran & Manivannan, 2016).…”

Section: Monitoreo De Vibracionesunclassified

Adquisición De Señales De Vibración Y Emisión Acústica Para El Diagnóstico De Severidad De Fallos en Maquinaria Rotativa

Córdova

Sánchez

Cabrera

et al. 2019

RCTA

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Se adquirió señales de vibración y emisión acústica para el diagnóstico de la severidad de fallos en maquinaria rotativa. Las señales de vibración y emisión acústica fueron adquiridas de una caja de engranajes de una etapa donde se simularon fallos de grieta y gripado en los engranes con un total de nueve niveles de severidad para cada fallo. Como resultados se analizó las señales en el dominio de la frecuencia donde se compara el comportamiento de la severidad del fallo de las señales de emisión acústica y vibraciones.

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Frequency domain averaging based experimental evaluation of gear fault without tachometer for fluctuating speed conditions

Cited by 41 publications

References 25 publications

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

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

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Adquisición De Señales De Vibración Y Emisión Acústica Para El Diagnóstico De Severidad De Fallos en Maquinaria Rotativa

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