**5. Conclusions**

In this paper, we adopt a new intelligent fault diagnosis method based on sPSDAE-CNN. Through a matrix transformation of the data collected from the UAV flight experiment, the one-dimensional time-series signal is transformed into two-dimensional gray image data, which expands the dimension of the sample and enhances the processing ability of the DL model. Secondly, by introducing a sparse pruning stack noise reduction autoencoder, the accuracy of a fault diagnosis algorithm in a high noise environment can be improved, and the input dimension of CNN data can also be reduced. In addition, pruning operation is used to reduce the complexity of the encoder, which can make the encoder converge quickly when minimizing the loss function. The combination of sPSDAE and the convolutional neural network can greatly improve the robustness and generalization ability of the fault diagnosis model. In order to verify the effectiveness of the model, this paper chooses CNN, SVM, and SDAE to compare. The experimental results show that under the condition of normal experimental data, sPSDAE-CNN has good results compared with other algorithms, but when the noise signal in the signal gradually begins to increase, the performance of other algorithms decreases significantly. Among them, when the signal-to-noise ratio reaches −4 dB, sPSDAE-CNN still has an accuracy of about 90%, the accuracy of the other three algorithms decreased to less than 80%, and SVM is less than 60%. Therefore, the fault diagnosis sPSDAE-CNN algorithm used in this paper can be used as a fault diagnosis method of four-rotor UAV in an actual high noise environment.

The method proposed in the article first converts a one-dimensional time-domain signal into a two-dimensional grayscale image, which expands the dimensionality of the data and can improve the ability of subsequent algorithms to extract features from the data. Secondly, the method of resampling was used to enhance the flight data of the quad-rotor UAV, which greatly improved the problem of the insufficient data set. Finally, the sparse pruning noise reduction autoencoder is introduced to perform noise reduction, dimensionality reduction, and feature extraction on the data. After processing, the noise in the original data can be filtered to a large extent, and the pruning operation can also improve the model—the calculation efficiency and noise reduction performance. All the data used in the article are balanced data sets. In the actual environment, it is impossible for all data to be unbalanced data sets. In the follow-up research, the application scope of unbalanced data sets will be further expanded.

In addition, in this paper, balanced data sets are used, but during the actual UAV mission, the data we collect can not be completely balanced data sets. Therefore, in future research, we will improve and expand the application scope of the algorithm based on the performance of sPSDAE-CNN on unbalanced data sets.

Secondly, the data used in this paper are all offline data collected at the end of the UAV flight. At present, it is not possible to collect the data of four-rotor UAV in real-time in the actual flight process to realize fault diagnosis. In future research, we can try to diagnose the fault of UAV in real-time and online with the algorithm used in this paper; this problem needs to be further studied and solved.

**Author Contributions:** Conceptualization, C.W.; methodology, P.Y.; software, C.W.; validation, C.W.; formal analysis, C.W.; investigation, H.G.; resources, P.Y.; data curation, H.G.; writing—original draft preparation, C.W. and P.Y.; writing—review and editing, P.Y.; visualization, P.L.; supervision, P.L.; project administration, P.Y.; funding acquisition, P.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by Key Laboratories for National Defense Science and Technology (6142605200402), the Aeronautical Science Foundation of China (20200007018001), the National Natural Science Foundation of China (61922042), the Aero Engine Corporation of China Industry University Research Cooperation Project (HFZL2020CXY011), and the Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures (Nanjing University of Aeronautics and Astronautics) MCMS-I-0121G03. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsoring agency.

**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.
