# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 6 Conclusions  
To improve the online fault diagnosis ability and accurately and efficiently identify the fault types in the running process of the motor, a fault diagnosis model combining multi-sensor data layer fusion and 1D-CNN multi-sensor feature layer fusion based on the correlation adaptive weighting method is proposed in this paper. Firstly, this paper proposes a correlation adaptive weighting method to fuse multi-source similar sensors information by the data layer. This method can retain a large amount of original data and provide the target with as fine information as possible to reduce the loss of fault features in the signal. Secondly, in this paper, a deep learning model (one-dimensional convolutional neural network) is adopted to carry out feature layer fusion of multi-source heterogeneous sensor information after data layer fusion, which can not only realize information compression and improve real-time performance but also maximize the characteristic information required for decision analysis. Finally, the proposed fault diagnosis model is tested on the experimental transmission platform of a three-phase asynchronous AC motor. The results are shown as follows:  
1. The motor fault diagnosis model proposed in this paper, which is based on the combination of multi-sensor data layer fusion and feature layer fusion, could accurately identify the current fault category according to the collected multi-source sensor data with an accuracy rate of 99.3%.
2. Compared with traditional fault diagnosis methods based on single sensor and shallow learning, the multi-sensor information fusion diagnosis model based on deep learning had higher diagnosis efficiency and accuracy, and its fault diagnosis accuracy reached 99.3%. Compared with the improved SVM, BP neural network, and single  
Figure 11: Three-dimensional cluster scatter diagram.  
sensor analysis method, the accuracy rate of this method is improved by 36.22%, 18.2%, and 13.01%, respectively.
3. After the data layer fusion of multi-source homogeneous sensors using the correlation adaptive weighting method was carried out, the fault information of multi-sensors could be fully extracted, which was conducive to improving the accuracy rate of fault diagnosis.  
In this paper, multi-sensor data layer fusion, feature layer fusion, and deep learning algorithms are applied to motor fault diagnosis technology, which promoted the application of multi-information fusion and artificial intelligence technology in the field of motor fault diagnosis and intelligent operation and maintenance. Compared with the traditional fault diagnosis methods, the proposed method can accurately identify motor faults with simpler operation logic and higher efficiency.  
Conceptualization, Y.G.; methodology, Y.Z.; writing--review and editing, Y.Z.; funding acquisition, C.L.; supervision, M.Y.; formal analysis, Y.G. All authors have and editing, M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the [China Postdoctoral Science Foundation] grant number [2021M700529], [Natural Science Basic Research Program of Shaanxi Province] grant number [2022]Q-434], [Fundamental Research Funds for the Central Universities] grant number [300102252110].  
Not applicable.  
The experimental data used in this paper are from the autonomous measurement of the autonomous driving and maglev technology equipment team of Chang'an University.  
The authors are very grateful for all the constructive comments that helped us improve the original version of the manuscript.  
The authors declare no conflict of interest.