Diagnosis of Induced Gear Wear in Two-Stage Helical Gearbox Based on Vibration Analysis

Brethee, Khaldoon F.; Ibrahim, Ghalib R.; Mohammed, Rashaq A.; Albarbar, Alhussein

doi:10.1109/dese51703.2020.9450757

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Moreover, since the vibration signals can be directly acquired from the gearbox, this technique results more useful for dealing with incipient fault conditions [ 17 ]. Therefore, an important number of works using vibration analysis have been developed, following a transform-based scheme, similar to the one used with the stator current signals [ 18 , 19 , 20 , 21 ]. Even though vibration analysis can improve the accuracy in the detection of wear condition in gears, the transform-based techniques still have some drawbacks.…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

Elvira-Ortiz

Saucedo-Dorantes

Osornio-Ríos

et al. 2023

Entropy

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Gears are reliable and robust elements that are found in any power transmission system. However, gears are prone to present incipient faults, such as wear, since they are constantly subjected to contact forces. Due to gears playing a key role in many industrial processes, it is important to develop condition monitoring strategies that ensure the proper functioning of the related power transmission system and the overall components. In this regard, the data on entropy provide relevant information that allow us to identify and quantify the effect of different wear levels in gears. Therefore, in this work, we proposed the use of seven entropy-related features to perform the identification of different wear severities in a gearbox. The novelty of this proposal lies in the use of the entropy features to carry out a high-performance characterization of the available vibration signals that are acquired from experimental tests. The novelty of this proposal lies in the fusion of three different techniques: entropy features, linear discriminant analysis, and artificial neural networks to obtain a machine learning approach for improving the detection of different wear severities in gears compared to other reported methodologies. This situation is achieved due to the high-performance characterization of the available vibration signals that are acquired from experimental tests. Additionally, the entropy features are subjected to a feature space transformation by means of linear discriminant analysis to obtain a 2D representation and, finally, the set of features extracted by linear discriminant analysis are used as inputs of a neural network-based classifier to determine the severity of wear that is present in the gears. The proposed methodology is validated and compared with a conventional statistical approach to show the improvement in the classification.

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Section: Introductionmentioning

confidence: 99%

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

Elvira-Ortiz

Saucedo-Dorantes

Osornio-Ríos

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

A Digital Twin-Based State Monitoring Method of Gear Test Bench

Wang

Yang

et al. 2023

Applied Sciences

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The gear test bench is important equipment for analyzing gear performance, detecting gear quality, and providing basic data for gear design and machining. In order to monitor the running state of the gear test bench, predict its running performance both timely and effectively, and guarantee its stable operation, a digital twin-based state monitoring method of the gear test bench is proposed in this paper. The state monitoring system of the gear test bench based on the digital twin model is constructed to simulate its normal running state in real time. On this basis, through the acquisition of physical information of the gear test bench, the developing of the state monitoring system, and the simulation of the digital twin model, the real-time state monitoring of the normal operation gear test bench is realized. The test results of the closed power flow gear test bench show that the digital twin simulation results of the gear test bench are basically consistent with it. The proposed state monitoring system can map the normal running state of the gear test bench, so as to realize the running state monitoring of the gear test bench.

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Diagnosis of Induced Gear Wear in Two-Stage Helical Gearbox Based on Vibration Analysis

Cited by 2 publications

References 25 publications

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

An Entropy-Based Condition Monitoring Strategy for the Detection and Classification of Wear Levels in Gearboxes

A Digital Twin-Based State Monitoring Method of Gear Test Bench

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