Detection of Local Gear Tooth Defects on a Multistage Gearbox Operating Under Fluctuating Speeds Using DWT and EMD Analysis

Inturi, Vamsi; Pratyush, Akula Sai; Sabareesh, G. R.

doi:10.1007/s13369-021-05807-0

Cited by 16 publications

(10 citation statements)

References 25 publications

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“…Nonetheless, a value of 0 or close to 0, indicates a weak or no correlation between them. [1] The IMFs can be categorized as noise-part, signal-part and trend-part. Typically, the noise is captured by the IMFs with low indices, and the trend is captured by the IMFs with de high indices.…”

Section: Signal Processingmentioning

confidence: 99%

“…Gearboxes are an equipment that is widely used in several industries such as aircrafts, automobiles, wind turbines, ship industries among others [1,29]. It plays an important role in industries for motion and torque transmission.…”

Section: Introductionmentioning

confidence: 99%

“…In order to overcome the previous limitations, the Empirical Mode Decomposition (EMD) has been proposed as a signal processing tool, because it's self-adaptive nature, i.e. EMD decomposes a complex signal into a set of Intrinsic Mode Functions (IMFs) based in its local maxima and minima [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox

Felix

Martins

Monteiro

et al. 2023

Preprint

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Gearboxes are widely used in various industries such as aircrafts, automobiles, wind turbines, ship industries among others. Due its complex configuration, it is a challenging task to identify fault and failures patterns. Its internal components, such as bearings and gears, have different fault patterns, that can appear in one or in both components. The vibration signals were processed using the Empirical Mode Decomposition (EMD) and the Pearson Correlation Coefficient (PCC) to select the significant IMFs and then 18 features were extract from this IMFs. Four features ranking techniques (ReliefF, Chi-Square, Max Relevance Min Redundancy (mRMR) and Decision Tree) were used in a committee to select the best feature set, among the 10 with the highest rank, that appears at least in 3 of the 4 methods. The new feature set was used as an input to Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Networks (ANN) algorithms. The results showed that the use of the PCC value as a tool for selecting the significant IMFs, combined with the feature committee led to good results for this classification problem. In this case study, the ANN model outperformed the SVM and the RF algorithms, by using only 4 features to achieve 95.42% of accuracy and 6 features to achieve 100% of accuracy.

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Section: Signal Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox

Felix

Martins

Monteiro

et al. 2023

Preprint

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“…However, these methods suffer greatly due to the mode-mixing problem and are computationally expensive. 4,23 The majority of the researchers have focused on decomposing the raw signatures through the approaches mentioned above and extracted statistical health indicators. Eventually, irrespective of the approaches, these health indicators are subjected to data-driven classification to discriminate among the various health conditions of gearbox.…”

Section: Background and Literature Surveymentioning

confidence: 99%

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Inturi

Balaji

Gyanam

et al. 2022

Structural Health Monitoring

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The vibration and acoustic signals collected from rotating machinery are often non-stationary and aperiodic, and it is a challenge to post-process and extract the defect sensitive health indicators. In this paper, we demonstrate how the estimated Hurst exponent of such measured data can be advantageous for analyzing non-stationary and aperiodic data due to its self-similarity and scale-invariance properties. To illustrate this, the paper demonstrates detection of fault diagnostics of a multi-stage gearbox operating under fluctuating speeds through estimated Hurst exponent of the raw vibration and acoustic signals as a health indicator. Thirteen health states of the gearbox are considered, and the raw vibration and acoustic signals are collected. The Hurst exponents are calculated using three different approaches: generalized Hurst exponent (q = 1, 2, 3, and 4), rescale range statistical (R/S) analysis, and dispersion analysis from the vibration and acoustic signals. Three different health indicator datasets are formulated and subjected to feature learning through conventional machine-learning (decision tree and support vector machine) and advanced machine-learning (deep-learning) classifiers. The effectiveness of these datasets while discriminating between the health states of the gearbox is investigated, yielding classification accuracies of 96.4% when compared with the individual health indicator datasets. The ability of the fault diagnosis and defect severity analysis with reduced reliance on the signal post-processing algorithms is demonstrated.

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“…Many researchers have done a lot of research on gear fault diagnosis using traditional intelligent diagnosis methods [ 5 , 6 , 7 , 8 , 9 , 10 ]. Inturi et al [ 11 ] extracted effective features of gear fault signals using DWT and EMD and input the extracted features into the SVM model for training. After completing several experiments, the accuracy rate obtained by combining SVM and DWT was better than that obtained by combining SVM and EMD.…”

Section: Introductionmentioning

confidence: 99%

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

Huo

Kang

Wang

et al. 2022

Entropy

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The gearbox is an important component in the mechanical transmission system and plays a key role in aerospace, wind power and other fields. Gear failure is one of the main causes of gearbox failure, and therefore it is very important to accurately diagnose the type of gear failure under different operating conditions. Aiming at the problem that it is difficult to effectively identify the fault types of gears using traditional methods under complex and changeable working conditions, a fault diagnosis method based on multi-sensor information fusion and Visual Geometry Group (VGG) is proposed. First, the power spectral density is calculated with the raw frequency domain signal collected by multiple sensors before being transformed into a power spectral density energy map after information fusion. Second, the obtained energy map is combined with VGG to obtain the fault diagnosis model of the gear. Finally, two datasets are used to verify the effectiveness and generalization ability of the method. The experimental results show that the accuracy of the method can reach 100% at most on both datasets.

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Detection of Local Gear Tooth Defects on a Multistage Gearbox Operating Under Fluctuating Speeds Using DWT and EMD Analysis

Cited by 16 publications

References 25 publications

A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox

A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Gear Fault Diagnosis Method Based on Multi-Sensor Information Fusion and VGG

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