Comparison of acoustic signal–based fault detection of mechanical faults in induction motors using image classification models

Boztas, Gullu

doi:10.1177/01423312231171664

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…Recently, infrared thermography (IRT) has emerged as an interesting alternative to monitoring IM machines, due to its measurement method being: (1) contactless, (2) noninvasive, and (3) the sensor can be located at a distant location [30]. In this sense, different applications have been reported [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

Trejo-Chavez,

Cruz-Albarran,

Resendiz-Ochoa

et al. 2023

Machines

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Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments.

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Section: Introductionmentioning

confidence: 99%

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

Trejo-Chavez,

Cruz-Albarran,

Resendiz-Ochoa

et al. 2023

Machines

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SSGAN: A Semantic Similarity-Based GAN for Small-Sample Image Augmentation

Ma,

Mi,

Gao

et al. 2024

Neural Process Lett

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Image sample augmentation refers to strategies for increasing sample size by modifying current data or synthesizing new data based on existing data. This technique is of vital significance in enhancing the performance of downstream learning tasks in widespread small-sample scenarios. In recent years, GAN-based image augmentation methods have gained significant attention and research focus. They have achieved remarkable generation results on large-scale datasets. However, their performance tends to be unsatisfactory when applied to datasets with limited samples. Therefore, this paper proposes a semantic similarity-based small-sample image augmentation method named SSGAN. Firstly, a relatively shallow pyramid-structured GAN-based backbone network was designed, aiming to enhance the model’s feature extraction capabilities to adapt to small sample sizes. Secondly, a feature selection module based on high-dimensional semantics was designed to optimize the loss function, thereby improving the model’s learning capacity. Lastly, extensive comparative experiments and comprehensive ablation experiments were carried out on the “Flower” and “Animal” datasets. The results indicate that the proposed method outperforms other classical GANs methods in well-established evaluation metrics such as FID and IS, with improvements of 18.6 and 1.4, respectively. The dataset augmented by SSGAN significantly enhances the performance of the classifier, achieving a 2.2% accuracy improvement compared to the best-known method. Furthermore, SSGAN demonstrates excellent generalization and robustness.

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Additive fault diagnosis techniques in rotor systems: a state-of-the-art review

Kumar,

Tiwari

2024

Sādhanā

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Comparison of acoustic signal–based fault detection of mechanical faults in induction motors using image classification models

Cited by 4 publications

References 43 publications

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

A CNN-Based Methodology for Identifying Mechanical Faults in Induction Motors Using Thermography

SSGAN: A Semantic Similarity-Based GAN for Small-Sample Image Augmentation

Additive fault diagnosis techniques in rotor systems: a state-of-the-art review

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