A Review of Gear Fault Diagnosis Using Various Condition Indicators

Sharma, Vikas; Parey, Anand

doi:10.1016/j.proeng.2016.05.131

Cited by 211 publications

(99 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Он получил название спектральной энтропии и широко используется в различных приложениях, таких как: распознавание речи, медицинская энцефалография, механика, климатические исследования. [12,13,14,15] Для произвольного спектра f i , i = 1, . .…”

Section: результаты расчетовunclassified

On the correlation of time series in ecology of aquatic systems

Тихонов

Medvinsky²

2019

KIAM Prepr.

View full text Add to dashboard Cite

A method for estimating the mutual influence of short time series, which is based on extension of the concept of spectral entropy to the spectra of cross-correlation functions is developed. Algorithms of calculations and programs are given. The time series of plankton abundance in the lake ecosystem, which is situated in the Republic of Belarus, are analyzed in order to identify mutual correlations. Correlations between the bacterioplankton abundance and variations in seasonal temperature are identified. The peculiarities of interrelations between the abundances of zooplankton/phytoplankton and temperature depending on the reservoir are analysed.

show abstract

Section: результаты расчетовunclassified

On the correlation of time series in ecology of aquatic systems

Тихонов

Medvinsky²

2019

KIAM Prepr.

View full text Add to dashboard Cite

show abstract

“…From the acoustics time domain signals, various statistical features were computed [26][27][28][29] using LabVIEW programme. Brief description of these features is presented in Table 1 and sample values shown in Table 2.…”

Section: Statistical Features Of Acoustic Signalmentioning

confidence: 99%

Application of psychoacoustics for gear fault diagnosis using artificial neural network

Kane

Andhare

2016

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

Identification of correct working of gearbox is a very important function during end of line inspection in the assembly line while manufacturing the gearbox. Such inspection is performed by an operator by listening to the sound of gearbox while running it on a test bench. Based on the sound emitted by the gearbox combined with experience and judgment of the operator, the gearbox is passed or rejected for fitting inside the vehicle. This paper makes an attempt to use artificial intelligence techniques to identify gearbox condition in the above environment by using psychoacoustic features to replace human hearing. Experiments are carried out on a gearbox test rig and sound data are acquired for good and faulty gear conditions. Psychoacoustic features and statistical indices are extracted from the data and these are then used as input to an artificial neural network. The artificial neural network output is the condition of gearbox. Performances of psychoacoustic and statistical indices are then compared. It is found that psychoacoustic features are able to predict gearbox condition with an accuracy of 99% and 98% for good and faulty conditions, respectively, whereas the statistical features are able to do the same with 97% and 98% accuracy. Therefore, it is concluded that psychoacoustic features have the potential to be used for the end of line inspection of gearbox in manufacturing environment and the process of inspection can be made objective by eliminating operator's ability and judgment.

show abstract

“…Compared with ordinary gear train [4] to [6], the complex structure of the composite gear train inevitably leads to its complex vibration characteristics. Its vibration signals contain vibration characteristics [7] and [8] of both ordinary gear train and planetary gear train as well as coupled vibration. Therefore, it is of significance to investigate effective fault diagnosis methods for gearboxes with composite gear trains.…”

Section: Introductionmentioning

confidence: 99%

A Fault Feature Extraction Method for Gearbox with Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelets Neighboring Coefficients

Pang¹,

Cheng²,

Zhao-jian³

et al. 2018

SV-JME

View full text Add to dashboard Cite

Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train. Highlights• This paper proposes a fault feature extraction method based on EEMD and translation-invariant multiwavelet neighbouring coefficients. • This method can be used for gearboxes with composite gear trains. • This method was verified in a test rig with a composite gear train. • The results showed this method is suitable not only for single fault but also for mixed faults.

show abstract

A Review of Gear Fault Diagnosis Using Various Condition Indicators

Cited by 211 publications

References 38 publications

On the correlation of time series in ecology of aquatic systems

On the correlation of time series in ecology of aquatic systems

Application of psychoacoustics for gear fault diagnosis using artificial neural network

A Fault Feature Extraction Method for Gearbox with Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelets Neighboring Coefficients

Contact Info

Product

Resources

About