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This paper presents a deep neural network (DNN) approach for induction motor fault diagnosis. The approach utilizes sparse auto-encoder (SAE) to learn features, which belongs to unsupervised feature learning that only requires unlabeled measurement data. With the help of the denoising coding, partial corruption is added into the input of the SAE to improve robustness of feature representation. Features...
Aiming at achieving composite materials damage identification of Structure Health Monitoring (SHM), an preprocessing algorithm of Redundant Second Generation Wavelet (RSGW) considering neighboring coefficients is introduced, which can denoises Lamb wave signals from noise flooding and structure complexity conditions for damage features extraction and further calculations. The practicability is validated...
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