Abstract: A cross-domain mechanical fault diagnosis method based on multi-channel feature fusion of a CBAM and use thereof includes conducting preliminary feature extraction in a grey-scale graph formed by original signals with convolutional neural network, obtaining high-level features, and compressing the high-level features with a full-connection layer module; conducting deep-level multi-sensor feature extraction with an improved convolutional block attention module (CBAM); conducting fusion for multi-sensor features extracted with an improved convolutional block attention module and obtaining multi-sensor fusion features; and inputting the multi-sensor fusion features into a tag assignor for fault diagnosis results. In the present invention, the latest multi-channel domain adaptation fault diagnosis method is used to realize efficiently intelligent fault diagnosis tasks of bearings in different working states.

Abstract: The present invention belongs to the technical field of mechanical fault data recognition, and discloses a cross-domain mechanical fault diagnosis method based on multi-channel feature fusion of an improved CBAM and use thereof, wherein the method comprises: conducting preliminary feature extraction in a grey-scale graph formed by original signals with convolutional neural network, obtaining high-level features, and compressing the high-level features with a full-connection layer module; conducting deep-level multi-sensor feature extraction with an improved convolutional block attention module (CBAM); conducting fusion for multi-sensor features extracted with an improved convolutional block attention module and obtaining multi-sensor fusion features; and inputting the multi-sensor fusion features into a tag assignor for fault diagnosis results.

Abstract: New pharmaceutical compounds, which are novel aminopyrrolizinone analogues, having anti-inflammatory and analgesic function and method of making the same.