Shaft Crack Monitoring by Using Acoustic Emission Technique

Seemuang, Nopparat; Lim, Jiraphong; Kaewkongka, Tonphong

doi:10.1109/icei18.2018.8448985

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Another identification method adds a shallow neural network to the traditional method. The structure of the artificial neural network commonly used for intelligent identification of axle fatigue cracks is shallow, which means that the structure of the artificial neural network contains only one hidden layer, such as the artificial neural network in [16,19]. Such simple architectures limit ANN's ability to learn complex non-linear relationships in axle crack identification problems.…”

Section: Dbn-based Intelligent Identification Methodsmentioning

confidence: 99%

“…Marks et al [18] conducted an experimental study of AE data captured during mechanical testing of railway axles and clustered the data using a self-organizing map to distinguish between damage signals from other sources. Seemuang et al [19] developed a shaft crack detection system using AE. The results show that shaft cracks can be successfully detected at an early stage before failure using this crack detection system.…”

Section: Introductionmentioning

confidence: 99%

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An acoustic emission identification model for train axle fatigue cracks based on deep belief network

Lin,

Tang,

Zhu

et al. 2024

Meas. Sci. Technol.

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Aiming to effectively identify train axle fatigue cracks, some scholars are now trying to consider introducing acoustic emission detection technology into axle health monitoring and combining it with intelligent neural networks to achieve better monitoring results. In the field of axle fatigue crack acoustic emission identification, the commonly used methods include parameter analysis, wavelet analysis, and traditional artificial neural networks. Though these methods did work in the field of axle fatigue crack identification, they still have two deficiencies. （1）The features are manually extracted depending on large artificial experience. (2) The methods mentioned above have shallow architectures, which cannot handle some complex fatigue crack linear relationships. In this paper, an acoustic emission signal analysis method for axle fatigue cracks based on deep belief networks is proposed to overcome the aforementioned deficiencies. The main purpose of this paper is to emphasize the feasibility of the deep belief network to intelligently identify fatigue crack signals from different types of acoustic emission signals of axles and to classify crack signals at different stages. The research method is to use acoustic emission (AE) technology to obtain the axle fatigue crack AE signal, pretreat the collected time-domain signal, identify whether cracks appear, and classify the stages of crack emergence by deep belief network (DBN) so as to find the axle crack in advance. The experimental analysis results show that the method of identification of axle fatigue cracks based on DBN not only can identify fatigue crack signals but also has a very high identification rate of fatigue cracks at different stages. In the axle fatigue crack acoustic emission identification field, it can be seen that the proposed method in this paper will be a promising approach.

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Section: Dbn-based Intelligent Identification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%