Pub Date : 2024-07-26DOI: 10.1088/1361-6501/ad67fa
Yadukrishnan Sasikumar, William Nuttall, Nigel J Mason
This paper describes the design, construction, and testing of an experimental rig capable of conducting high-temperature adsorption isotherm analysis on large quantities of powder samples inside an ultra-high vacuum chamber. Data is obtained by dosing in fixed amounts of water vapor and measuring precise pressure changes using a high-temperature capacitance manometer. The rig is designed to provide insight into the wetting of failed Spent Nuclear Fuel (SNF) under conditions conventionally regarded as “dry”. Validation experiments are reported based on powder CeO2 (a non-radioactive surrogate for SNF) at 100○C. It is planned that this and successor rigs can provide ever more direct experimental evidence to address a key policy–relevant problem.
{"title":"An Experimental Apparatus to Study the Adsorption of Water on Proxies for Spent Nuclear Fuel Surfaces","authors":"Yadukrishnan Sasikumar, William Nuttall, Nigel J Mason","doi":"10.1088/1361-6501/ad67fa","DOIUrl":"https://doi.org/10.1088/1361-6501/ad67fa","url":null,"abstract":"\u0000 This paper describes the design, construction, and testing of an experimental rig capable of conducting high-temperature adsorption isotherm analysis on large quantities of powder samples inside an ultra-high vacuum chamber. Data is obtained by dosing in fixed amounts of water vapor and measuring precise pressure changes using a high-temperature capacitance manometer. The rig is designed to provide insight into the wetting of failed Spent Nuclear Fuel (SNF) under conditions conventionally regarded as “dry”. Validation experiments are reported based on powder CeO2 (a non-radioactive surrogate for SNF) at 100○C. It is planned that this and successor rigs can provide ever more direct experimental evidence to address a key policy–relevant problem.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"14 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous development of computer technology, deep learning has been widely used in fault diagnosis and achieved remarkable results. However, in actual production, the problem of insufficient fault samples and the difference in data domains caused by different working conditions seriously limit the improvement of model diagnosis ability. In recent years, meta-learning has attracted widespread attention from scholars as one of the main methods of few-shot learning. It can quickly adapt to new tasks by training on a small number of samples. A fine-tuning prototypical network (FPN) is proposed on meta-learning methods to address the challenges of fault diagnosis under few-shot and cross-domain. Firstly, the shuffle attention (SA) is used to enhance the feature extraction ability of the network and suppress irrelevant features. Then, the support set of the target domain is split into two parts: pseudo support set and pseudo query set, which are used to fine-tune the prototypical network and improve the model generalization. Finally, experiments are conducted on three rotating equipment datasets to verify the method's effectiveness.
{"title":"A Fine-Tuning Prototypical Network for Few-shot Cross-domain Fault Diagnosis","authors":"Jianhua Zhong, Kairong Gu, Haifeng Jiang, Wei Liang, Shuncong Zhong","doi":"10.1088/1361-6501/ad67f5","DOIUrl":"https://doi.org/10.1088/1361-6501/ad67f5","url":null,"abstract":"\u0000 With the continuous development of computer technology, deep learning has been widely used in fault diagnosis and achieved remarkable results. However, in actual production, the problem of insufficient fault samples and the difference in data domains caused by different working conditions seriously limit the improvement of model diagnosis ability. In recent years, meta-learning has attracted widespread attention from scholars as one of the main methods of few-shot learning. It can quickly adapt to new tasks by training on a small number of samples. A fine-tuning prototypical network (FPN) is proposed on meta-learning methods to address the challenges of fault diagnosis under few-shot and cross-domain. Firstly, the shuffle attention (SA) is used to enhance the feature extraction ability of the network and suppress irrelevant features. Then, the support set of the target domain is split into two parts: pseudo support set and pseudo query set, which are used to fine-tune the prototypical network and improve the model generalization. Finally, experiments are conducted on three rotating equipment datasets to verify the method's effectiveness.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"11 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1088/1361-6501/ad67f6
Zhenfa Shao, Hong Jiang, Xiangfeng Zhang, Jianyu Zhou, Hu X
In practical scenarios, gearbox fault diagnosis faces the challenge of extremely scarce labeled data. Additionally, variations in operating conditions and differences in sensor installations exacerbate data distribution shifts, significantly increasing the difficulty of fault diagnosis. To address the above issues, this paper proposes a Wavelet Dynamic Joint Self-Adaptive Network guided by a Pseudo-Label Alignment Mechanism (WDJSN-DFL). First, the Wavelet-Efficient Convolution Module (WECM) is designed based on wavelet convolution and efficient attention mechanisms. This module is used to construct a multi-wavelet convolution feature extractor to extract critical fault features at multiple levels. Secondly, to improve the classifier's discriminability in the target domain, a transitional clustering-guided pseudo-label alignment mechanism (DFL) is developed. This mechanism can capture fuzzy classification samples and improve the pseudo-label quality of the target domain. Finally, a dynamic joint adaptive algorithm (DJSN) is proposed, which is composed of Joint Maximum Mean Square Discrepancy (JMSD) and Joint Maximum Mean Discrepancy (JMMD). The algorithm can adaptively adjust according to the dynamic balance factor to minimize the domain distribution discrepancy. Experiments on two different gearbox datasets show that WDJSN-DFL performs better in diagnostic scenarios under varying load conditions and different sensor installation setups, validating the proposed method's effectiveness and superiority.
{"title":"Application of wavelet dynamic joint adaptive network guided by pseudo-label alignment mechanism in gearbox fault diagnosis","authors":"Zhenfa Shao, Hong Jiang, Xiangfeng Zhang, Jianyu Zhou, Hu X","doi":"10.1088/1361-6501/ad67f6","DOIUrl":"https://doi.org/10.1088/1361-6501/ad67f6","url":null,"abstract":"\u0000 In practical scenarios, gearbox fault diagnosis faces the challenge of extremely scarce labeled data. Additionally, variations in operating conditions and differences in sensor installations exacerbate data distribution shifts, significantly increasing the difficulty of fault diagnosis. To address the above issues, this paper proposes a Wavelet Dynamic Joint Self-Adaptive Network guided by a Pseudo-Label Alignment Mechanism (WDJSN-DFL). First, the Wavelet-Efficient Convolution Module (WECM) is designed based on wavelet convolution and efficient attention mechanisms. This module is used to construct a multi-wavelet convolution feature extractor to extract critical fault features at multiple levels. Secondly, to improve the classifier's discriminability in the target domain, a transitional clustering-guided pseudo-label alignment mechanism (DFL) is developed. This mechanism can capture fuzzy classification samples and improve the pseudo-label quality of the target domain. Finally, a dynamic joint adaptive algorithm (DJSN) is proposed, which is composed of Joint Maximum Mean Square Discrepancy (JMSD) and Joint Maximum Mean Discrepancy (JMMD). The algorithm can adaptively adjust according to the dynamic balance factor to minimize the domain distribution discrepancy. Experiments on two different gearbox datasets show that WDJSN-DFL performs better in diagnostic scenarios under varying load conditions and different sensor installation setups, validating the proposed method's effectiveness and superiority.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"50 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-26DOI: 10.1088/1361-6501/ad67f7
Yixin Zhou, Baisheng Wu, Zeyao Chen, Congwen Zhong, H. Zhong
Measurement over pins plays a crucial role in ensuring the quality in gear manufacturing, which requires the evolution of the inverse involute function. In this paper, an efficient algorithm for calculating the inverse involute function is proposed. Firstly, the initial approximate solution of the pressure angle near 0 rad is established by using Padé approximation and one step of Schröder iteration. Then the involute equation is transformed into another form by introducing a variable transformation, and the initial approximate solution of the pressure angle near π/2 rad is constructed by using the same method. The piecewise initial approximate solution over the entire interval of expanded angle is obtained by combining these solutions. The accuracy of the initial approximate solution can be significantly improved by using one step of Schröder iteration again. A numerical example of two-pin measurement is presented to show the superiority of our method over traditional iterative approach. The algorithm has high efficiency and accuracy, and is a useful tool for designers to accurately calculate the gear system.
对销测量在确保齿轮制造质量方面起着至关重要的作用，而这需要反渐开线函数的演化。本文提出了一种计算反渐开线函数的高效算法。首先，利用 Padé 近似和一步 Schröder 迭代建立 0 rad 附近压力角的初始近似解。然后，通过引入变量变换将渐开线方程转化为另一种形式，并用同样的方法建立 π/2 rad 附近压力角的初始近似解。综合这些解，就得到了整个扩展角区间的片断初始近似解。通过再次使用一步 Schröder 迭代法，可以显著提高初始近似解的精度。通过一个双针测量的数值示例，说明了我们的方法优于传统的迭代方法。该算法效率高、精度高，是设计人员精确计算齿轮系统的有用工具。
{"title":"Calculation of the inverse involute function and application to measurement over pins","authors":"Yixin Zhou, Baisheng Wu, Zeyao Chen, Congwen Zhong, H. Zhong","doi":"10.1088/1361-6501/ad67f7","DOIUrl":"https://doi.org/10.1088/1361-6501/ad67f7","url":null,"abstract":"\u0000 Measurement over pins plays a crucial role in ensuring the quality in gear manufacturing, which requires the evolution of the inverse involute function. In this paper, an efficient algorithm for calculating the inverse involute function is proposed. Firstly, the initial approximate solution of the pressure angle near 0 rad is established by using Padé approximation and one step of Schröder iteration. Then the involute equation is transformed into another form by introducing a variable transformation, and the initial approximate solution of the pressure angle near π/2 rad is constructed by using the same method. The piecewise initial approximate solution over the entire interval of expanded angle is obtained by combining these solutions. The accuracy of the initial approximate solution can be significantly improved by using one step of Schröder iteration again. A numerical example of two-pin measurement is presented to show the superiority of our method over traditional iterative approach. The algorithm has high efficiency and accuracy, and is a useful tool for designers to accurately calculate the gear system.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"52 42","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1088/1361-6501/ad678a
Ryan Thomas, Brian Salmon, Damien Holloway, Jan C. Olivier
This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawater irradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated data were generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the sphere in 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeable materials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receiver coil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms to noise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression, which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models. Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram data used for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heaviside step-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects, including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified using finite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion of electromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without the use of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallic spheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paper presents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.
{"title":"Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses","authors":"Ryan Thomas, Brian Salmon, Damien Holloway, Jan C. Olivier","doi":"10.1088/1361-6501/ad678a","DOIUrl":"https://doi.org/10.1088/1361-6501/ad678a","url":null,"abstract":"\u0000 This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawater irradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated data were generated using an analytical model of a magnetic dipole in air and seawater placed 1.5 – 3.5 m above the center of the sphere in 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeable materials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receiver coil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms to noise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression, which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models. Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram data used for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heaviside step-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects, including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified using finite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion of electromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without the use of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallic spheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paper presents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"108 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1088/1361-6501/ad6784
Yongkui Sun, Yuan Cao, Peng Li, Shuai Su
Railway point machines (RPMs) are one of the safety-critical equipments closely related to train operation safety. Due to their high failure rate, it is urgent to develop an effective diagnosis method for RPMs. Considering the easy-to-collect and anti-interference characteristics of vibration signals, this paper develops a vibration-based diagnosis method. First, to address the difficulty of multi-scale permutation entropy in characterizing the fault information contained in the derivatives of the raw signal, novel feature named derivative multi-scale permutation entropy is designed, which can further complete the fault information of RPMs. Second, to further improve the diagnosis accuracy of support vector machine (SVM), a decision fusion strategy based on three feature sets is developed, which can further improve the diagnosis accuracy, especially in the normal-reverse direction. Finally, the effect and superiority of the proposed method are verified based on the collected vibration signals from Xi'an Railway Signal Co.,Ltd by experiment comparisons. The diagnosis accuracies of reverse-normal and normal-reverse directions reach 99.43% and 100% respectively, indicating its superiority.
{"title":"Diagnosis for Railway Point Machines Using Novel Derivative Multi-Scale Permutation Entropy and Decision Fusion Based on Vibration Signals","authors":"Yongkui Sun, Yuan Cao, Peng Li, Shuai Su","doi":"10.1088/1361-6501/ad6784","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6784","url":null,"abstract":"\u0000 Railway point machines (RPMs) are one of the safety-critical equipments closely related to train operation safety. Due to their high failure rate, it is urgent to develop an effective diagnosis method for RPMs. Considering the easy-to-collect and anti-interference characteristics of vibration signals, this paper develops a vibration-based diagnosis method. First, to address the difficulty of multi-scale permutation entropy in characterizing the fault information contained in the derivatives of the raw signal, novel feature named derivative multi-scale permutation entropy is designed, which can further complete the fault information of RPMs. Second, to further improve the diagnosis accuracy of support vector machine (SVM), a decision fusion strategy based on three feature sets is developed, which can further improve the diagnosis accuracy, especially in the normal-reverse direction. Finally, the effect and superiority of the proposed method are verified based on the collected vibration signals from Xi'an Railway Signal Co.,Ltd by experiment comparisons. The diagnosis accuracies of reverse-normal and normal-reverse directions reach 99.43% and 100% respectively, indicating its superiority.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"53 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1088/1361-6501/ad6785
Fei Yan, Gao Ze, Tian Ye, Wen Jie, Jia Liu
To address the problems of low efficiency, large error and high bit error rate (BER) in the phase unwrapping of high-frequency fringes by the traditional time-phase unwrapping method, in this paper we propose a phase coding method that quantizes the multivariate gray code in the phase domain. Instead of embedding the stepped phase into a sinusoidal pattern, we embed the multivariate gray code pattern into a sinusoidal pattern, which reduces the gray levels in the phase coding pattern to a larger extent and widens the longitudinal phase width between each step in the coding pattern. After the camera captures the deformed coding pattern, the deformed multivariate gray code is dequantified by the phase difference and the gray level, and the high-quality high-frequency ladder code word is obtained by decoding the quantized multivariate gray code.In addition, the step code word is superimposed with the binary wrapped phase and then filtered to obtain a correction code word for correcting the phase error. Through simulations and experiments, we comprehensively compare the proposed method with various classical phase unwrapping methods. The effectiveness of the proposed method is verified in terms of the decoding error, the measurement effect, and the projection pattern.
{"title":"Three-dimensional measurements based on multivariate gray code phase encoding","authors":"Fei Yan, Gao Ze, Tian Ye, Wen Jie, Jia Liu","doi":"10.1088/1361-6501/ad6785","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6785","url":null,"abstract":"\u0000 To address the problems of low efficiency, large error and high bit error rate (BER) in the phase unwrapping of high-frequency fringes by the traditional time-phase unwrapping method, in this paper we propose a phase coding method that quantizes the multivariate gray code in the phase domain. Instead of embedding the stepped phase into a sinusoidal pattern, we embed the multivariate gray code pattern into a sinusoidal pattern, which reduces the gray levels in the phase coding pattern to a larger extent and widens the longitudinal phase width between each step in the coding pattern. After the camera captures the deformed coding pattern, the deformed multivariate gray code is dequantified by the phase difference and the gray level, and the high-quality high-frequency ladder code word is obtained by decoding the quantized multivariate gray code.In addition, the step code word is superimposed with the binary wrapped phase and then filtered to obtain a correction code word for correcting the phase error. Through simulations and experiments, we comprehensively compare the proposed method with various classical phase unwrapping methods. The effectiveness of the proposed method is verified in terms of the decoding error, the measurement effect, and the projection pattern.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"28 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the finite element method (FEM) was used to study the reflected and transmitted waves of laser-generated Rayleigh waves from subsurface inclined cracks, the propagation paths and mode conversion mechanisms of different characteristic waves are determined. The Rayleigh wave will interact with the crack top tip and propagate back and forth along the crack surface, and be converted to shear waves at the crack top tip. The shear waves will mode-convert to Rayleigh waves at the free surface when the incidence angle of the shear wave is larger than 60°. Moreover, for the Rayleigh wave interacting with the crack bottom tip, when the crack inclined angle is less than 60°, some Rayleigh waves will travel along the crack surface to the crack top tip. When the crack inclination angle is greater than 60°, in addition to the Rayleigh waves propagating upwards along the crack surface, some Rayleigh waves convert to shear waves at the crack bottom tip and then incident on the free surface of the workpiece. Experiments were carried out to validate some of the Rayleigh wave propagation paths. The experimental results matched the theoretical arrival time well, thus verifying the reliability of the analytical wave path. The results are helpful for the quantitative detection of subsurface inclined cracks using laser ultrasonic techniques.
{"title":"Study on the interaction mechanism of laser-generated Rayleigh waves and subsurface inclined cracks","authors":"Chuanyong Wang, Fumin Zhang, Yuan-Liu Chen, Wen Wang, Yun Wang, Keqing Lu, Yuanping Ding, Yinliang Shen, Bing-Feng Ju","doi":"10.1088/1361-6501/ad6787","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6787","url":null,"abstract":"\u0000 In this paper, the finite element method (FEM) was used to study the reflected and transmitted waves of laser-generated Rayleigh waves from subsurface inclined cracks, the propagation paths and mode conversion mechanisms of different characteristic waves are determined. The Rayleigh wave will interact with the crack top tip and propagate back and forth along the crack surface, and be converted to shear waves at the crack top tip. The shear waves will mode-convert to Rayleigh waves at the free surface when the incidence angle of the shear wave is larger than 60°. Moreover, for the Rayleigh wave interacting with the crack bottom tip, when the crack inclined angle is less than 60°, some Rayleigh waves will travel along the crack surface to the crack top tip. When the crack inclination angle is greater than 60°, in addition to the Rayleigh waves propagating upwards along the crack surface, some Rayleigh waves convert to shear waves at the crack bottom tip and then incident on the free surface of the workpiece. Experiments were carried out to validate some of the Rayleigh wave propagation paths. The experimental results matched the theoretical arrival time well, thus verifying the reliability of the analytical wave path. The results are helpful for the quantitative detection of subsurface inclined cracks using laser ultrasonic techniques.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"30 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1088/1361-6501/ad6789
Zimeng Wang, Bingwei Zhang, Kaiyong Jiang, Junyi Lin
In order to realize single-frame three-dimensional (3D) reconstruction, a single-frame two-step phase-shift method based on orthogonal composite pattern projection is proposed to solve the problem that the traditional N-step phase-shift profilometry needs multiple projections for 3D reconstruction. The orthogonal composite pattern uses only two carrier channels to reduce the spectrum overlapping influence on the demodulation accuracy of carrier and modulated fringes. A two-dimensional variational mode decomposition (2DVMD) method is adopted to remove the background DC component of the sinusoidal fringe to overcome the mode overlap problem by controlling the size of the bandwidth. Thus, the two-step phase-shift method is applied to calculate the phases for 3D reconstruction. The experimental results show that, compared with the typical Fourier Transform Profilometry (FTP) method , 3-step composite method and 2+1 composite method, the 3D reconstruction accuracy of the proposed method is improved by 49.1% ,31.4% and 23.2% respectively according to Mean Absolute Error (MAE), and by 73.0%, 58.4% and 56.8% respectively according to Mean Squared Error (MSE) as the evaluation index. Finally, the dynamic 3D reconstruction experiment demonstrates the good adaptability of dynamic 3D reconstruction.
{"title":"3D reconstruction with single-frame two-step phase-shift method based on orthogonal composite fringe pattern projection","authors":"Zimeng Wang, Bingwei Zhang, Kaiyong Jiang, Junyi Lin","doi":"10.1088/1361-6501/ad6789","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6789","url":null,"abstract":"\u0000 In order to realize single-frame three-dimensional (3D) reconstruction, a single-frame two-step phase-shift method based on orthogonal composite pattern projection is proposed to solve the problem that the traditional N-step phase-shift profilometry needs multiple projections for 3D reconstruction. The orthogonal composite pattern uses only two carrier channels to reduce the spectrum overlapping influence on the demodulation accuracy of carrier and modulated fringes. A two-dimensional variational mode decomposition (2DVMD) method is adopted to remove the background DC component of the sinusoidal fringe to overcome the mode overlap problem by controlling the size of the bandwidth. Thus, the two-step phase-shift method is applied to calculate the phases for 3D reconstruction. The experimental results show that, compared with the typical Fourier Transform Profilometry (FTP) method , 3-step composite method and 2+1 composite method, the 3D reconstruction accuracy of the proposed method is improved by 49.1% ,31.4% and 23.2% respectively according to Mean Absolute Error (MAE), and by 73.0%, 58.4% and 56.8% respectively according to Mean Squared Error (MSE) as the evaluation index. Finally, the dynamic 3D reconstruction experiment demonstrates the good adaptability of dynamic 3D reconstruction.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"21 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-25DOI: 10.1088/1361-6501/ad6786
昭宇 涂, Zeyu Luo, Menghui Li, Jun Wang, Zhi-xin Yang, Xianbo Wang
Signal preprocessing and feature extraction are decisive factors in determining the frequency of bearing faults. The presence of noise interference in the status signal of rolling bearings often hampers accurate fault detection. Although there are various methods for preprocessing vibration signals in rolling bearings, they need further improvement in terms of enhancing fault feature expression and localizing fault frequency bands. This limitation significantly hinders the accuracy of fault frequency determination. In order to enhance the representation of fault information on the frequency spectrum, this study proposes a combined approach that incorporates sparse stacked autoencoder (SSAE), wavelet packet decomposition (WPD), and adaptive spectrum amplitude modulation (ASAM). The resulting method is referred to as SSAE-WPD-ASAM. Firstly, the bearing vibration signal is decomposed by wavelet packet according to the scale and frequency band of the signal. On this basis, the signal reconstruction is realized based on the wavelet packet coefficient and energy distribution in different frequency bands. Secondly, for the whole life cycle signal, the reconstructed signal is self-encoded by sparse stacked autoencoder to achieve dimensionality reduction of the reconstructed signal. Then, the spare reconstructed signal is subjected to adaptive spectrum amplitude modulation (ASAM). Finally, through envelope demodulation, peak detection of fault frequency and empirical fault frequency comparison, the specific fault types of rolling bearings are determined. The proposed method is verified by theoretical simulation and three groups of practical experiments. The results show that the proposed method has a significant improvement in diagnostic efficiency and accuracy compared with traditional diagnostic methods.
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