**6. Conclusions**

In the paper, the fault diagnosis for motor vibration signals has been investigated based on spatiotemporal feature fusion. The method has used gated recurrent units and convolutional neural networks to extract the temporal and spatial features of vibration signals. Since the time series of vibration signals were too long to retain all the key information, a GRU has extracted the temporal features by an attention mechanism to effectively synthesize the states of different time series and the vibration features at different moments. When extracting spatial features, the one-dimensional time-domain signal has been converted into a two-dimensional matrix using local mean decomposition and matrix transformation to extend the data dimensionality. The CNN model based on the attention mechanism adaptively has extracted the channel and location features of the signal. In the experimental evaluation of eight different vibration signals, the vibration signal processing method combined with spatiotemporal feature fusion has obtained 99.75% recognition accuracy. The method has improved the diagnostic performance effectively, which is important for the safe detection and stable operation of the system.

**Author Contributions:** Conceptualization, L.W. and F.X.; methodology, L.W.; software, C.Z.; validation, L.W., C.Z., J.Z. and F.X.; writing—review and editing, L.W., C.Z., J.Z. and F.X.; visualization, F.X.; supervision, F.X.; project administration, F.X.; funding acquisition, L.W. and F.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by the Heilongjiang Province Key R&D Program (GA21A304), the Fundamental Research Funds in Heilongjiang Provincial Universities (135509404 and 135409102), and the Open project of Agricultural multidimensional sensor information Perception of Engineering and Technology Center in Heilongjiang Province (DWCGQKF202105).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
