Applications of Artificial Intelligence for Fault Diagnosis of Rotating Machines: A Review

Kibrete, Fasikaw; Woldemichael, Dereje Engida

doi:10.1007/978-3-031-28725-1_4

Cited by 6 publications

(2 citation statements)

References 164 publications

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“…Convolutional neural networks (CNNs) are biologically inspired feedforward neural networks that operate by extracting local features from raw input data in a layer-by-layer fashion to make predictions [70]. Their inception can be traced back to 1980 [71], and further advancements were made in 1998 [72].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Kibrete,

Trzepieciński,

Gebremedhen

et al. 2023

J. Compos. Sci.

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The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Kibrete,

Trzepieciński,

Gebremedhen

et al. 2023

J. Compos. Sci.

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“…Faults of transmission components directly affect the operational reliability of rotating machinery and may cause significant accidents, resulting in substantial economic losses and even casualties [3]. Therefore, it is of great research value to carry out fault diagnosis and predictive maintenance of rotating machinery [4].…”

Section: Introductionmentioning

confidence: 99%

A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN

Wang,

Zhu,

2023

IEEE Access

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Fault diagnosis is critical to maintaining the performance of rotating machinery and ensuring the safe operation of the equipment. Convolutional neural networks (CNNs) have recently shown great potential with excellent automatic feature learning and nonlinear mapping abilities in the field of rotating machinery fault diagnosis. However, the CNN-based methods still suffer from some defects, such as inadequate data utilization and uneconomical computational efficiency, which limits further improvement of diagnosis performance. Therefore, this paper proposes a fault diagnosis method based on multi-sensor fusion and Convolutional Neural Network with Efficient Channel Attention (ECA-CNN). First, multi-sensor vibration signals are sampled, converted, and channel fused into multi-channel images with rich and comprehensive features. Then, the efficient channel attention mechanism is introduced into CNN to increase the feature learning ability by adaptively scoring and assigning weights to the channel features. The ECA-CNN is proposed to learn representative fault features from multi-sensor fusion data to achieve fault identification. Finally, two experimental cases on the bearing and gearbox datasets prove that the proposed method has excellent performance, strong generalization capability, and high computational efficiency.INDEX TERMS Fault diagnosis, multi-sensor fusion, convolutional neural networks, channel attention mechanism.

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Bearing Fault Diagnosis Based on Artificial Intelligence Methods: Machine Learning and Deep Learning

Ghorbel,

Eddai,

Limam

et al. 2024

Arab J Sci Eng

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Applications of Artificial Intelligence for Fault Diagnosis of Rotating Machines: A Review

Cited by 6 publications

References 164 publications

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN

Bearing Fault Diagnosis Based on Artificial Intelligence Methods: Machine Learning and Deep Learning

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