Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions

Sharma, Vikas; Parey, Anand

doi:10.1016/j.measurement.2016.04.076

Cited by 45 publications

(17 citation statements)

References 31 publications

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“…For this purpose, in order to identify localized gear faults, several CIs can be used for the evaluation of the vibration signal peakiness, which is correlated with the severity of the localized fault. In this study, the following standard CIs have been considered: kurtosis and Crest Factor (CF) [9,36]. Furthermore, the effectiveness of the proposed CIs, that is, CPF and NSVP, has been tested for the simulated vibration signals and the real case studies.…”

Section: Problem Statement and The Proposed Methodsmentioning

confidence: 99%

“…A number of researches can be found in the literature about the development of parameters for the quantification of the vibration signature modification [4,9]. These parameters are generally called condition indicators (CIs).…”

Section: Gear Fault Identification By Condition Indicatorsmentioning

confidence: 99%

“…The success of the fault identification strongly depends on the employed signal processing techniques, the system typology under investigation, and the working condition. In fact, the state of the art about the identification of localized gear faults covers a wide range of different approaches such as the following: the cyclostationary theory [2][3][4], which takes advantage of the hidden periods embodied in the vibration signals; the Kurtogram [5] for the selection of the frequency band associated with the maximum Spectral Kurtosis; time-frequency signal representations like Continuous Wavelet Transform [6]; the blind deconvolution algorithms [7,8], which estimate the excitation source due to the presence of the fault from the noisy observation; condition indicators based on the Time Synchronous Average [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

Buzzoni

Mucchi

D’Elia

et al. 2017

Shock and Vibration

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The gear fault diagnosis on multistage gearboxes by vibration analysis is a challenging task due to the complexity of the vibration signal. The localization of the gear fault occurring in a wheel located in the intermediate shaft can be particularly complex due to the superposition of the vibration signature of the synchronous wheels. Indeed, the gear fault detection is commonly restricted to the identification of the stage containing the faulty gear rather than the faulty gear itself. In this context, the paper advances a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the vibration signals of the synchronous gears mounted on the same shaft. The physical meaningful modes are selected by means of a criterion based on Pearson's coefficients and the fault detection is performed by dedicated condition indicators. The proposed method is validated taking into account simulated vibrations signals and real ones.

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Section: Problem Statement and The Proposed Methodsmentioning

confidence: 99%

Section: Gear Fault Identification By Condition Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

Buzzoni

Mucchi

D’Elia

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…There are many studies focused on condition monitoring and relevant technological innovations. As vibration analysis is still the most popular approach, many studies have contributed to signal-processing methods for condition monitoring, such as the short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) [10,11] and wavelet transform (WT) [12,13]. Alternatively, the S-transform is superior in the time-frequency analyses of non-stationary signals, which eliminates the limitations of STFT and WT [14].…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Zhu

et al. 2019

Energies

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Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

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“…Meanwhile, on the one hand, the gear vibration signals obtained from the variable speed condition, that is, the acceleration or deceleration processes, often can contain more fault information compared to the stationary processes, which can detect the gear faults earlier. On the other hand, the gear vibration signals obtained from the acceleration or deceleration processes are nonstationary signals, which have low signal-to-noise ratio (SNR) in practice [11][12][13][14], which make it difficult to obtain the gear vibration signals' fault features. Hence, the diagnosis of gear faults by analyzing the gear vibration signals obtained from the variable speed condition has been a hot research topic recently.…”

Section: Introductionmentioning

confidence: 99%

Gear Fault Diagnosis in Variable Speed Condition Based on Multiscale Chirplet Path Pursuit and Linear Canonical Transform

Zhang

Chai

et al. 2018

Complexity

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The vibration signals analysis is a very effective and reliable method for detecting the gear failures. Because the vibration signals acquired from the gear in the variable speed condition often contain more useful fault information, the analysis of the gear vibration signals during the variable speed condition has been a hot research topic. In this paper, a method based on the multiscale chirplet path pursuit (MSCPP) and the linear canonical transform (LCT) has been applied to diagnose the gear fault in the variable speed condition for the first time. First, by using the MSCPP method to estimate the instantaneous meshing frequency, the suitable signal segment approximation to the acceleration or deceleration process can be selected. Then, because the LCT is a novel and efficient nonstationary signals analysis tool, the optimal LCT spectrum of the selected signal has been attainted to diagnose the gear faults based on the properties of the LCT. In addition, the simulations and the experimental evaluation are provided to verify the effectiveness of the proposed method.

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Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions

Cited by 45 publications

References 31 publications

Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Gear Fault Diagnosis in Variable Speed Condition Based on Multiscale Chirplet Path Pursuit and Linear Canonical Transform

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