## 7 Conclusions  
For the classification and detection of rotating machinery faults, this paper proposes a method of combining adaptive learning dictionary with 1 DCNN, which realizes the effective classification and diagnosis of different fault types of rotating machinery under noise interference. The main conclusions are as follows:  
Aiming at the noise reduction of sparse representation preprocessing, an adaptive learning dictionary is constructed by using the K-SVD algorithm; the original signal is sparsely decomposed and reconstructed by the optimized OMP algorithm, the feature signal is extracted effectively, and the signal is reconstructed to reduce the influence of noise. Then, the preprocessed signal is adaptively extracted from deeper signal features by 1 DCNN, and the fault is classified. Finally, the classification accuracy of the two groups of experimental data reaches 99.82% and 99.76%, respectively. Compared with other machine learning methods, the results show that the proposed method is effective and feasible for fault classification and the diagnosis of rotating machinery parts such as rolling bearings and worm gears.  
The SR-1 DCNN network model proposed in this paper has fast convergence speed and strong robustness in the test of the Case Western Reserve University (CWRU) rolling bearing database and worm gear reducer dataset. The noise environment interference with a different SNR is added to the test set to simulate the influence of noise on the classification accuracy in the actual industrial environment. The experimental results show that this method can well identify the faults of rotating machinery such as bearings and worm gears. It is verified that the model has good generalization ability and strong anti-noise ability.  
In the future work, the author will further study the method based on the combination of preprocessing denoising and network feature extraction and classification. Through the study of the existing technology, we can avoid or reduce the possible impact on the classification accuracy caused by manual processing as much as possible. In addition, in the case of very large noise interference, the classification accuracy is not very good, which may be due to the loss of some features due to sparse representation noise reduction, which affects the classification accuracy. We will study a better evaluation standard.  
Conceptualization, H.W. and C.L.; methodology, H.W. and C.L.; software, H.W. and C.L.; validation, H.W., C.L. and W.D.; formal analysis, C.L.; investigation, C.L.; resources, H.W., W.D. and S.W.; data curation, H.W. and C.L.; writing--original draft preparation, C.L.; writing--review and editing, C.L., H.W. and W.D.; visualization, C.L.; supervision, H.W., W.D. and S.W.; project administration, H.W. and C.L.; funding acquisition, W.D. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the National Natural Science Foundation of china (approved grant: U1804141).  
Publicly available datasets [42] were analyzed in this study, other datasets are proprietary and cannot be shared at this time.  
The authors declare no conflict of interest.