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This study presents a novel fault diagnosis framework for rotating machinery, combining a residual network (ResNet) and an extreme learning machine (ELM) classifier, termed RNELM. The framework utilizes continuous wavelet transform (CWT) to convert raw signals into time-frequency images, allowing for improved feature extraction and fault diagnosis performance. Experimental results demonstrate that RNELM outperforms traditional methods across multiple datasets, showcasing its effectiveness and practicality i

Author

yarrietbrito

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EN