A comprehensive survey on applications of AI technologies to failure analysis of industrial systems

Bi, Siguo; Wu, Bochun; Hu, Shuyan; Huang, Wutao; Ni, Wei; Gong, Yi; Wang, Xin

doi:10.1016/j.engfailanal.2023.107172

Cited by 9 publications

(1 citation statement)

References 114 publications

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“…The key transmission components of rotating machinery (e.g., bearings and gears) are inevitably subject to wear, corrosion, deformation, cracks, and other failures under complex operating environments such as heavy loads and high speeds [2]. 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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Section: Introductionmentioning

confidence: 99%