# A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM
## 4 Conclusions  
This paper proposes a novel approach for grinding wheel wear recognition based on VMD denoising and the AO-CNN-LSTM algorithm, aiming to improve the recognition rate of different wear states of grinding wheels in noisy environments. Drawing on previous research experience, the variation in AE signals is crucial for determining the grinding wheel wear state. Therefore, this study employs the method of VMD decomposition combined with wavelet denoising reconstruction, which proves effective in handling AE in noisy environments, making the dataset more reliable. For feature extraction and processing, this paper utilizes the CNN-LSTM architecture based on AO-optimized parameters. The experimental results indicate that the method achieves an overall recognition accuracy of 98.8%, with precision rates exceeding 99% for both initial wear and intermediate wear classifications, and a recall rate of 100% for severe wear. In future research, we will continue to collect more wear data from different grinding conditions and different types of grinding wheels, aiming to enhance the efficiency of AE signal processing and improve the universality of the grinding wheel wear recognition model. The main contributions of this study are highlighted as follows:  
1. The paper successfully introduces a new method based on AE, signal processing, and deep learning, effectively identifying grinding wheel wear states.
2. The VMD denoising method in this paper significantly reduces the impact of noise on AE, enhancing the effectiveness of the sample dataset.
3. The comprehensive neural network AO-CNN-LSTM, combining CNN and LSTM, with the introduction of AO-optimized parameters. This neural network, when processing AE signals during grinding wheel abrasion, is more adept at capturing temporal information than traditional neural networks, exhibiting stronger spatiotemporal modeling capabilities.
4. AO enhances the global search capabilities of neural network parameter optimization, aiding in finding optimal model configurations. Its robust adaptability and convergence speed further enhance recognition accuracy. This study provides valuable insights into the combination of AO with various deep learning architectures.  
Figure 20: Confusion Matrix for CNN.  
**Author Contributions:** Conceptualization, K.X.; methodology, K.X. and D.F.; software, D.F.; validation, K.X.; formal analysis, K.X. and D.F.; investigation, D.F.; resources, K.X.; data curation, K.X.; writing--original draft preparation, D.F.; writing--review and editing, K.X. and D.F.; visualization, D.F.; supervision, K.X.; project administration, K.X.; funding acquisition, K.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 52275054, 51205108 and 51575161.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets presented in this article are not readily available because the data are part of an ongoing study.  
**Acknowledgments:** The authors wish to thank Henan University of Science and Technology for their assistance and support with this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.