# A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM
###### Abstract  
Monitoring the condition of the grinding wheel in real-time during the grinding process is crucial as it directly impacts the precision and quality of the workpiece. Deep learning technology plays a vital role in analyzing the changes in sensor signals and identifying grinding wheel wear during the grinding process. Therefore, this paper innovatively proposes a grinding wheel wear recognition method based on Variational Mode Decomposition (VMD) denoising and Aquila Optimizer--Convolutional Neural Network--Long Short-Term Memory (AO-CNN-LSTM). The paper utilizes Acoustic Emission (AE) signals generated during grinding to identify the condition of the grinding wheel. To address noise interference, the study introduces the VMD algorithm for denoising the sample dataset, enhancing the effectiveness of neural network training. Subsequently, the dataset is fed into the designed Convolutional Neural Network--Long Short-Term Memory (CNN-LSTM) structure with AO-optimized parameters. Experimental results demonstrate that this method achieves high accuracy and performance. acoustic emission signal; Aquila Optimizer; Convolutional Neural Network; long short-term memory; Variational Mode Decomposition +
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Subsequently, grinding experiments under different grinding conditions were conducted on a flat grinder to validate the accuracy of the monitoring and identification system [5]. [PERSON] et al. introduced a method for accurate and timely identification of grinding wheel wear in external cylindrical grinding using the Hilbert Huang Transform (HHT) and support vector machine. Treating grinding signals as time series data, they optimized grinding wheel wear-related feature selection through machine learning algorithms [6]. [PERSON] et al. presented an approach using pre-trained Convolutional Neural Networks (CNN) for automatic feature extraction. By extracting features tailored to different industrial grinding conditions, they tested the recognition of grinding wheel wear states using t-SNE and PCA clustering algorithms. The study confirmed the outstanding efficiency of the pre-trained CNN in automatically extracting features from frequency maps [7]. [PERSON] et al. employed long short-term memory (LSTM), a specialized type of Recurrent Neural Network (RNN), to construct a regression prediction model for wheel state, utilizing AE spectrums as the model input. The model demonstrated satisfactory prediction accuracy on the test set [8]. Additionally, [PERSON] et al. devised an intelligent system for grinding wheel condition monitoring, leveraging machining sound and CNN [9]. Traditional signal processing methods heavily rely on frequency domain analysis, and they perform inadequately in handling the intricate time-series signals of grinding wheel wear, especially in noisy environments. Some machine learning methods have made progress in the recognition of grinding wheel wear, but they often require manual feature extraction and exhibit relatively weak modeling capabilities for complex time-series data [10]. While CNNs have achieved significant success in image recognition, they tend to overlook temporal information when dealing with time-series data, thereby limiting their effectiveness in grinding wheel wear recognition. LSTMs can handle time-series data, but when used alone, they may lack sufficient extraction of frequency domain information, especially in complex wear conditions. Therefore, this paper introduces an innovative method for recognizing the wear state of grinding wheels, employing Variational Mode Decomposition (VMD) for noise reduction and the Aquila Optimizer--Convolutional Neural Network--Long Short-Term Memory (AO-CNN-LSTM) architecture to process the AE signal in a noisy environment. Firstly, in actual industrial production, signals generated during grinding wheel abrasion are often subject to environmental noise interference. To address this issue, this paper introduces the VMD algorithm.