On a Diagnostic Procedure to Automatically Classify Gear Faults Using the Vibration Signal Decomposition and Support Vector Machine

Dien, Nguyen Phong; Du, Nguyen Trong

doi:10.1007/978-3-030-04792-4_55

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Currently, intelligent classification techniques are outstanding methods such as artificial neural networks (ANNs), 7–15 fuzzy logic systems, 16–17 neuro-fuzzy, 18–22 parsimonious network based on fuzzy inference system (PANFIS), 23 and support vector machine (SVM). 24–29 Simulated by human thinking, ANN is an automatic tool for the detection and identification of different kinds of faults without in-depth knowledge of the system. Therefore, the combination of Autogram and ANN seems to be a new intelligent fault diagnosis approach that could detect, identify, and classify different types of gear faults.…”

Section: Introductionmentioning

confidence: 99%

New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network

Afia

Rahmoune

Benazzouz

et al. 2014

Advances in Mechanical Engineering

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Nowadays, fault detection, identification, and classification seem to be the most difficult challenge for gear systems. It is a complex procedure because the defects affecting gears have the same frequency signature. Thus, the variation in load and speed of the rotating machine will, inevitably, lead to erroneous detection results. Moreover, it is important to discern the nature of the anomaly because each gear defect has several consequences on the mechanism’s performance. In this article, a new intelligent fault diagnosis approach consisting of Autogram combined with radial basis function neural network is proposed. Autogram is a new sophisticated enhancement of the conventional Kurtogram, while radial basis function is used for classification purposes of the gear state. According to this approach, the data signal is decomposed by maximal overlap discrete wavelet packet transform into frequency bands and central frequencies called nodes. Thereafter, the unbiased autocorrelation of the squared envelope for each node is computed in order to calculate the kurtosis for each one at every decomposition level. Finally, the feature matrix obtained from the previous step will be the input of the radial basis function neural network to provide a new automatic gear fault diagnosis technique. Experimental results from the gearbox with healthy state and five different types of gear defects under variable speeds and loads indicate that the proposed method can successfully detect, identify, and classify the gear faults in all cases.

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Section: Introductionmentioning

confidence: 99%

New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network

Afia

Rahmoune

Benazzouz

et al. 2014

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

Gear Fault Classification Using the Vibration Signal Decomposition and Neural Networks

Dien

2020

Advances in Engineering Research and Application

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Advanced Signal Decomposition Methods for Vibration Diagnosis of Rotating Machines: A Case Study at Variable Speed

Dien

2021

Lecture Notes in Mechanical Engineering

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On a Diagnostic Procedure to Automatically Classify Gear Faults Using the Vibration Signal Decomposition and Support Vector Machine

Cited by 3 publications

References 11 publications

New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network

New intelligent gear fault diagnosis method based on Autogram and radial basis function neural network

Gear Fault Classification Using the Vibration Signal Decomposition and Neural Networks

Advanced Signal Decomposition Methods for Vibration Diagnosis of Rotating Machines: A Case Study at Variable Speed

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