Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression

Zhang, Chuandong; Wang, Wei; Li, Hai

doi:10.1016/j.measurement.2021.110466

Cited by 33 publications

(12 citation statements)

References 60 publications

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“…Furthermore, a GP can make the model very flexible through the design of different kernel functions, and the appropriate kernel function can be selected according to the actual physical process. This method is widely used in mechanical performance evaluation, life prediction, financial stock trends, and other fields [ 31 , 32 , 33 , 34 ].…”

Section: Methodsmentioning

confidence: 99%

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

Yin

Zhou

Liang

2022

Sensors

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Residual stress is closely related to the evolution process of the component fatigue state, but it can be affected by various sources. Conventional fatigue evaluation either focuses on the physical process, which is limited by the complexity of the physical process and the environment, or on monitored data to form a data-driven model, which lacks a relation to the degenerate process and is more sensitive to the quality of the data. This paper proposes a fusion-driven fatigue evaluation model based on the Gaussian process state–space model, which considers the importance of physical processes and the residuals. Through state–space theory, the probabilistic space evaluation results of the Gaussian process and linear physical model are used as the hidden state evaluation results and hidden state change observation function, respectively, to construct a complete Gaussian process state–space framework. Then, through the solution of a particle filter, the importance of the residual is inferred and the fatigue evaluation model is established. Fatigue tests on titanium alloy components were conducted to verify the effectiveness of the fatigue evaluation model. The results indicated that the proposed models could correct evaluation results that were far away from the input data and improve the stability of the prediction.

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Section: Methodsmentioning

confidence: 99%

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

Yin

Zhou

Liang

2022

Sensors

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“…In order to improve the classification performance, the maximum singular value of the enhanced image is chosen as the feature for extraction. According to equation (10), the grayscale matrix of the enhanced image is subjected to singular value decomposition, and the maximum singular value is selected as the feature parameter. Similarly, feature extraction was performed 10 times for each operating condition, and the results are presented in table 4.…”

Section: Improved Canberra Distance Feature Analysismentioning

confidence: 99%

“…They transformed vibration signals into symmetrical dot patterns to differentiate different grinding states. Zhang et al utilized an improved symmetrical dot pattern (ISDP) in tool wear prediction [10]. They achieved effective tool wear prediction by utilizing ISDP.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images★

Wang,

Sun

2024

Eng. Res. Express

Self Cite

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In the fault diagnosis of mechanical equipment, vibrational signals often contain noise. In order to solve the problem of rolling bearing fault diagnosis under the influence of noise, an image enhancement method based on mean difference images is proposed. Convert one-dimensional time series into two-dimensional image data by using the symmetrized dot pattern method. The data are processed and grouped by variational mode decomposition to obtain a mean image with stable features. A feature enhancement method based on improved mean difference images is used to achieve data mining of fault features. The improved Canberra distance is used as the classification basis to realize the accurate classification of rolling bearing faults. Finally, the classification effect of the method is verified by experiments. The experimental results show that the image enhancement method proposed in this paper can improve fault features in the image and has a good anti-noise ability.

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“…Their conversion to the polar plot of SDP is shown in Figure 1b. The mapping relationship between the pieces of Figure 1a,b is given by the following equations [27].…”

Section: Fundamental Theories 21 Sdp Theorymentioning

confidence: 99%

Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG

Chen

Liu

et al. 2022

Applied Sciences

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A decision-level approach using multi-sensor-based symmetry dot pattern (SDP) analysis with a Visual Geometry Group 16 network (VGG16) fault diagnosis model for multi-source information fusion was proposed to realize accurate and comprehensive fault diagnosis of gearbox gear teeth. Firstly, the SDP technique was used to perform a feature-level fusion of the fault states of gearbox gear collected by multiple sensors, which could initially visualize the vibration states of the gear teeth in different states. Secondly, the SDP images obtained were combined with the deep learning VGG16. In this way, the local diagnostic results of each sensor can be easily obtained. Finally, the local diagnostic results of each sensor were combined with the DS evidence theory to achieve decision-level fusion, which can better realize comprehensive fault detection for gearbox gear teeth. Before fusion, the accuracies of the three sensors were 96.43%, 93.97%, and 93.28%, respectively. When sensor 1 and sensor 2 were fused, the accuracy reached 99.93%, which is 3.52% and 6.34% better than when using sensors 1 and 2, respectively, alone. When sensor 1 and sensor 3 were fused, the accuracy reached 99.96%, marking an improvement of 3.36% and 6.85% over individual use of sensors 1 and 3, respectively. When sensor 2 and sensor 3 were fused, the accuracy reached 99.40%, which is 5.78% and 6.56% better than individual use of sensors 2 and 3, respectively. When the three sensors were fused simultaneously, the accuracy reached 99.98%, which is 3.68%, 6.40%, and 7.18% better than individual use of sensors 1, 2, and 3, respectively.

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Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression

Cited by 33 publications

References 60 publications

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

A Gaussian Process State Space Model Fusion Physical Model and Residual Analysis for Fatigue Evaluation

Bearing fault diagnosis based on the feature enhancement of improved mean differential SDP images★

Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG

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