Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels

Gonzalez, Daniel J.; Alvarez, Jorge; Sánchez, J.A.; Godino, Leire; Pombo, I.

doi:10.3390/s22186911

Cited by 12 publications

(4 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. Guo Bi 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.…”

Section: Of 20mentioning

confidence: 72%

“…The SNR OUT and MSE of the denoised signals using EMD, ICEEMDAN, and the method employed in this paper are shown in Table 1. Formulas (7) and ( 8) illustrate the calculation, where n represents the sample quantity, x i denotes the original signal, and xi represents the denoised signal. The SNR OUT obtained by our method was higher than that of EMD and ICEEMDAN denoising, while the MSE was lower than that of EMD and ICEEMDAN.…”

Section: Vmd Algorithm For Denoising Ae Signalsmentioning

confidence: 99%

See 1 more Smart Citation

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

Xu,

Feng

2024

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Of 20mentioning

confidence: 72%

Section: Vmd Algorithm For Denoising Ae Signalsmentioning

confidence: 99%

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

Xu,

Feng

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Studies have shown that machine learning algorithms can effectively predict tool wear rates in various machining operations, such as milling [14], turning [15], and grinding [16]. Hybrid models combining ML algorithms with optimization techniques have also been proposed and shown to have high accuracy in predictions [17].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Tool Wear Prediction Accuracy Using Walsh–Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection

Shah

Borade

Sanghavi

et al. 2023

Sensors

View full text Add to dashboard Cite

Tool wear is an important concern in the manufacturing sector that leads to quality loss, lower productivity, and increased downtime. In recent years, there has been a rise in the popularity of implementing TCM systems using various signal processing methods and machine learning algorithms. In the present paper, the authors propose a TCM system that incorporates the Walsh–Hadamard transform for signal processing, DCGAN aims to circumvent the issue of the availability of limited experimental dataset, and the exploration of three machine learning models: support vector regression, gradient boosting regression, and recurrent neural network for tool wear prediction. The mean absolute error, mean square error and root mean square error are used to assess the prediction errors from three machine learning models. To identify these relevant features, three metaheuristic optimization feature selection algorithms, Dragonfly, Harris hawk, and Genetic algorithms, were explored, and prediction results were compared. The results show that the feature selected through Dragonfly algorithms exhibited the least MSE (0.03), RMSE (0.17), and MAE (0.14) with a recurrent neural network model. By identifying the tool wear patterns and predicting when maintenance is required, the proposed methodology could help manufacturing companies save money on repairs and replacements, as well as reduce overall production costs by minimizing downtime.

show abstract

“…The application of acoustic emission technology is also difficult, and the recording of acoustic emission signals may be affected by the environment and contact area to produce noise [14]. For this reason, scholars have proposed a variety of noise reduction methods.…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset

Zhao,

Ma,

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

In order to solve the noise reduction problem of acoustic emission signals with cracks, a method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and wavelet packet (WPT) is proposed and named CEEMD-WPT. Firstly, the single Empirical Mode Decomposition (EMD) used in the traditional CEEMD is improved into the WPT-EMD with a more stable noise reduction effect. Secondly, after decomposition, the threshold value of the correlation coefficient is determined for the Intrinsic Mode Function (IMF), and the low correlation component is further processed by WPT. In addition, in order to solve the problem that it is difficult to quantify the real signal noise reduction effect, a new quantization index “principal interval coefficient (PIC)” is designed in this paper, and its reliability is verified through simulation experiments. Finally, noise reduction experiments are carried out on the real crack acoustic emission dataset consisting of tensile, shear, and mixed signals. The results show that CEEMD-WPT has the highest number of signals with a principal interval coefficient of 0–0.2, which has a better noise reduction effect compared with traditional CEEMD and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Moreover, the statistical variance of CEEMD-WPT is evidently one order of magnitude smaller than that of CEEMD, so it has stronger stability.

show abstract

Deep Learning-Based Feature Extraction of Acoustic Emission Signals for Monitoring Wear of Grinding Wheels

Cited by 12 publications

References 31 publications

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

Enhancing Tool Wear Prediction Accuracy Using Walsh–Hadamard Transform, DCGAN and Dragonfly Algorithm-Based Feature Selection

Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset

Contact Info

Product

Resources

About