A Review on Engine Fault Diagnosis through Vibration Analysis

Abdeltwab, Nouby M. Ghazaly Mahrousa M.

doi:10.17762/ijrmee.v9i2.364

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…An important work in this field [9][10] [11], It made it possible to make an Advanced Neuroevolutionary Genetic Algorithm (ANGA) to improve protection. As the world of defense changes all the time, Smith et al said that we need to find new ways to make AI models more adaptable and useful.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Development of Neuroevolutionary Algorithms for Cyber Security and Optimizing AI Models through Genetic Programming

Dharmesh Dhabliya

2024

jes

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Different methods have been created to make optimization processes more efficient and effective, which is a big step forward in the area of evolutionary optimization. This abstract talks about four well-known methods: Neuroevolution of Augmenting Topologies (NEAT), Genetic Algorithms (GAs), Genetic Programming (GP), and Advanced Neuroevolutionary Genetic Algorithm (ANGA). It focuses on important performance indicators like Fitness Metrics, Generalization, Efficiency and Speed, and Overall Performance. With scores of 90% in Fitness Metrics and 88% in Generalization, NEAT, a neuroevolutionary program, shows strong success in competitive tasks. With an 80% score, it does poorly in Efficiency and Speed, though. GAs are known for using a population-based method. They do very well in Efficiency and Speed (90%), but they do a little worse in Fitness Metrics and Generalization (89% and 85%, respectively). With a focus on updated computer programs, GP gets marks that are equal in Fitness Metrics, Generalization, and Efficiency and Speed (88%, 85%, and 85%, respectively). The new ANGA algorithm stands out as a top worker, doing exceptionally well in all tests. ANGA gets great marks of 93% in Fitness Metrics, 94% in Generalization, and 93% in Efficiency and Speed. This shows how well it can optimize everything. Overall Performance score of 97.78% shows how well it works as a whole, making ANGA a potential method for genetic optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Design and Development of Neuroevolutionary Algorithms for Cyber Security and Optimizing AI Models through Genetic Programming

Dharmesh Dhabliya

2024

jes

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Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Gao,

Wang,

et al. 2024

Meas. Sci. Technol.

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Current self-attention based Transformer models in the field of fault diagnosis are limited to identifying correlation information within a single sequence and are unable to capture both time and frequency domain fault characteristics of the original signal. To address these limitations, this research introduces a two-channel Transformer fault diagnosis model that integrates time and frequency domain features through a cross-attention mechanism. Initially, the original time-domain fault signal is converted to the frequency domain using the Fast Fourier Transform, followed by global and local feature extraction via a Convolutional Neural Network. Next, through the self-attention mechanism on the two-channel Transformer, separate fault features associated with long distances within each sequence are modeled and then fed into the feature fusion module of the cross-attention mechanism. During the fusion process, frequency domain features serve as the query sequence Q, and time domain features as the key-value pairs K. By calculating the attention weights between Q and K, the model excavates deeper fault features of the original signal. Besides preserving the intrinsic associative information within sequences learned via the self-attention mechanism, the Twins Transformer also models the degree of association between different sequence features using the cross-attention mechanism. Finally, the proposed model's performance was validated using four different experiments on four bearing datasets, achieving average accuracy rates of 99.67%, 98.76%, 98.47%, and 99.41%. These results confirm the model's effective extraction of time and frequency domain correlation features, demonstrating fast convergence, superior performance, and high accuracy.

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An Evaluation of the research knowledge within the community of electrical engineers

Chauhan

Ansari

2022

2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC)

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A Review on Engine Fault Diagnosis through Vibration Analysis

Cited by 6 publications

References 19 publications

Design and Development of Neuroevolutionary Algorithms for Cyber Security and Optimizing AI Models through Genetic Programming

Design and Development of Neuroevolutionary Algorithms for Cyber Security and Optimizing AI Models through Genetic Programming

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

An Evaluation of the research knowledge within the community of electrical engineers

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