A Rotating Machinery Fault Diagnosis Method Based on Multi-Sensor Fusion and ECA-CNN
Hongxing Wang,
Hua Zhu,
Huafeng Li
Abstract: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 diagnos… Show more
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