Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.111996
Yifan Tang , Cheng Chen , Chenghui Wang , Shuyu Lin
Piezoelectric composites, consisting of piezoceramic and polymer materials, can reduce the brittleness and strength of ceramics and offer an innovative approach to improving the performance of ultrasonic transducers. Recent advances in piezoelectric composites have proposed a variety of transducers with different connectivity types, while spherical transducers composed of 1-3-2 piezoelectric composites have not yet been investigated. Here, we propose a 1-3-2 piezoelectric composite spherical transducer (1-3-2-PCST) capable of achieving broadband and omnidirectional radiation in breathing mode. The proposed design is composed of six identical spherically curved square piezoelectric composites. A universal analysis method for the 1-3-2-PCST based on the electromechanical equivalent circuit is derived. The effects of geometric dimensions and volume fraction of piezoceramic on the effective electromechanical coupling coefficient and resonance/anti-resonance frequency are investigated. Experiments and the finite element method validate the correctness of the universal analysis method. Our design bridges the gap between the spherical transducer and 1-3-2 piezoelectric composite and may have far-reaching implications for hydrophones, medical diagnosis, and ocean exploration.
{"title":"A universal analysis method for an omnidirectional broadband spherical transducer based on 1-3-2 piezoelectric composite","authors":"Yifan Tang , Cheng Chen , Chenghui Wang , Shuyu Lin","doi":"10.1016/j.ymssp.2024.111996","DOIUrl":"10.1016/j.ymssp.2024.111996","url":null,"abstract":"<div><div>Piezoelectric composites, consisting of piezoceramic and polymer materials, can reduce the brittleness and strength of ceramics and offer an innovative approach to improving the performance of ultrasonic transducers. Recent advances in piezoelectric composites have proposed a variety of transducers with different connectivity types, while spherical transducers composed of 1-3-2 piezoelectric composites have not yet been investigated. Here, we propose a 1-3-2 piezoelectric composite spherical transducer (1-3-2-PCST) capable of achieving broadband and omnidirectional radiation in breathing mode. The proposed design is composed of six identical spherically curved square piezoelectric composites. A universal analysis method for the 1-3-2-PCST based on the electromechanical equivalent circuit is derived. The effects of geometric dimensions and volume fraction of piezoceramic on the effective electromechanical coupling coefficient and resonance/anti-resonance frequency are investigated. Experiments and the finite element method validate the correctness of the universal analysis method. Our design bridges the gap between the spherical transducer and 1-3-2 piezoelectric composite and may have far-reaching implications for hydrophones, medical diagnosis, and ocean exploration.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 111996"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.111968
S. Kamali, A. Palermo, A. Marzani
An approach is proposed to improve anomaly detection of structural health monitoring systems by constructing “virtual baselines” for structures undergoing non-stationarities due to environmental and operational variability (EOV) and growing damage. The process requires a baseline dataset of structural damage-sensitive (SDS) parameters as well as environmental and operational (EO) variables. On this data, at first a regression model is trained with SDS parameters as the target dependent variables, and EO parameters as independent features. In contrast to classical models that rely solely on EO independent features, the proposed method incorporates the time information of the samples. This addition allows time to represent the progression of damage in the regression model, as time and damage growth are closely related.
The regression model is utilized to construct a virtual baseline by incorporating the corresponding EO parameters while fixing the time information to that of the initial sample. This approach preserves EO variations while setting the damage information to a constant value, specifically that of the first sample, which is assumed to represent minimum damage. The virtual baseline is then employed in the anomaly detection and EOV compensation process. Through examples on numerical and experimental datasets, with and without EOV compensation, the effectiveness of the proposed method is demonstrated, highlighting its capability to mitigate both damage-related and EOV-related non-stationarities from the baseline and improve the probability of damage detection.
{"title":"Virtual baseline to improve anomaly detection of SHM systems with non-stationary data","authors":"S. Kamali, A. Palermo, A. Marzani","doi":"10.1016/j.ymssp.2024.111968","DOIUrl":"10.1016/j.ymssp.2024.111968","url":null,"abstract":"<div><div>An approach is proposed to improve anomaly detection of structural health monitoring systems by constructing “virtual baselines” for structures undergoing non-stationarities due to environmental and operational variability (EOV) and growing damage. The process requires a baseline dataset of structural damage-sensitive (SDS) parameters as well as environmental and operational (EO) variables. On this data, at first a regression model is trained with SDS parameters as the target dependent variables, and EO parameters as independent features. In contrast to classical models that rely solely on EO independent features, the proposed method incorporates the time information of the samples. This addition allows time to represent the progression of damage in the regression model, as time and damage growth are closely related.</div><div>The regression model is utilized to construct a virtual baseline by incorporating the corresponding EO parameters while fixing the time information to that of the initial sample. This approach preserves EO variations while setting the damage information to a constant value, specifically that of the first sample, which is assumed to represent minimum damage. The virtual baseline is then employed in the anomaly detection and EOV compensation process. Through examples on numerical and experimental datasets, with and without EOV compensation, the effectiveness of the proposed method is demonstrated, highlighting its capability to mitigate both damage-related and EOV-related non-stationarities from the baseline and improve the probability of damage detection.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 111968"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.112021
Miaoshuo Li , Shixi Yang , Jun He , Xiwen Gu , Yongjia Xu , Fengshou Gu , Andrew D. Ball
While vision-based methods are renowned for their ability in full-field vibration measurements, accurately and robustly extracting subtle displacements remains a significant challenge. To address this, this paper presents a novel Optimal Phase-projection Wavelet Denoising (OPWD) method for vision-based vibration measurement that is adept at extracting characteristics of subtle displacement components. The OPWD method enhances signal quality through a structured three-step process: constructing a signal model from pixel array data, transforming this model into the frequency-space domain, and applying wavelet denoising in the spatial dimension. The method was validated through experimental comparisons on a structural beam, confirming consistency with the resonance frequencies obtained from accelerometers and mode shapes from finite element analysis. This study also contributes a comprehensive framework that lays the groundwork for future developments and implementations of additional methods in vision-based vibration measurement.
{"title":"Full-field extraction of subtle displacement components via phase-projection wavelet denoising for vision-based vibration measurement","authors":"Miaoshuo Li , Shixi Yang , Jun He , Xiwen Gu , Yongjia Xu , Fengshou Gu , Andrew D. Ball","doi":"10.1016/j.ymssp.2024.112021","DOIUrl":"10.1016/j.ymssp.2024.112021","url":null,"abstract":"<div><div>While vision-based methods are renowned for their ability in full-field vibration measurements, accurately and robustly extracting subtle displacements remains a significant challenge. To address this, this paper presents a novel Optimal Phase-projection Wavelet Denoising (OPWD) method for vision-based vibration measurement that is adept at extracting characteristics of subtle displacement components. The OPWD method enhances signal quality through a structured three-step process: constructing a signal model from pixel array data, transforming this model into the frequency-space domain, and applying wavelet denoising in the spatial dimension. The method was validated through experimental comparisons on a structural beam, confirming consistency with the resonance frequencies obtained from accelerometers and mode shapes from finite element analysis. This study also contributes a comprehensive framework that lays the groundwork for future developments and implementations of additional methods in vision-based vibration measurement.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112021"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.112024
Wenjun Gao , Yuanhao Li , Can Li , Yang Xu , Zhenxia Liu
In high-speed ball bearings, the revolution of spherical elements is significantly influenced by drag force of lubricant fluid, impacting the bearing’s dynamic and thermal performance. To investigate drag force in under-race lubrication ball bearings, a numerical study was conducted after the experimental verification. A multi-sphere flow model with a sandwich plate was tested, which indicates a strong agreement between numerical calculations and experimental data, with an error margin below 10 %. In the numerical simulation, pressure distribution and shear stress on the ball was studied, considering variables such as bearing rotational speed, oil flow rate, oil density, and oil viscosity. Results reveal low pressure at the upper hemisphere’s center and high pressure on both sides. Shear stress is concentrated in contact areas between the element and components like the inner ring, outer ring, and cage. Oil injection from the inner ring significantly alters the pressure and shear stress distribution in the lower hemisphere. The direction of drag force is the same as the rolling element’s revolution, acting as driving force for elements’ revolution. Increasing bearing rotating speed, oil flow rate, oil viscosity, and oil density all contribute to higher drag forces on the ball. Based on the numerical simulations, a predictive formula for the ball’s drag force was developed.
{"title":"Numerical prediction of drag force on spherical elements inside high-speed ball bearing with under-race lubrication","authors":"Wenjun Gao , Yuanhao Li , Can Li , Yang Xu , Zhenxia Liu","doi":"10.1016/j.ymssp.2024.112024","DOIUrl":"10.1016/j.ymssp.2024.112024","url":null,"abstract":"<div><div>In high-speed ball bearings, the revolution of spherical elements is significantly influenced by drag force of lubricant fluid, impacting the bearing’s dynamic and thermal performance. To investigate drag force in under-race lubrication ball bearings, a numerical study was conducted after the experimental verification. A multi-sphere flow model with a sandwich plate was tested, which indicates a strong agreement between numerical calculations and experimental data, with an error margin below 10 %. In the numerical simulation, pressure distribution and shear stress on the ball was studied, considering variables such as bearing rotational speed, oil flow rate, oil density, and oil viscosity. Results reveal low pressure at the upper hemisphere’s center and high pressure on both sides. Shear stress is concentrated in contact areas between the element and components like the inner ring, outer ring, and cage. Oil injection from the inner ring significantly alters the pressure and shear stress distribution in the lower hemisphere. The direction of drag force is the same as the rolling element’s revolution, acting as driving force for elements’ revolution. Increasing bearing rotating speed, oil flow rate, oil viscosity, and oil density all contribute to higher drag forces on the ball. Based on the numerical simulations, a predictive formula for the ball’s drag force was developed.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112024"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.112026
Bingyan Chen , Wade A. Smith , Yao Cheng , Fengshou Gu , Fulei Chu , Weihua Zhang , Andrew D. Ball
The establishment of probability distributions of machine vibration signals is crucial for calculating theoretical baselines of machine health indicators. Health indicators based on the envelope and squared envelope are an important family for condition monitoring. Under the assumption that the vibration signals of a good machine are Gaussian distributed, the envelope of a normal machine signal with zero mean is proven to follow a Rayleigh distribution with one parameter that depends on the noise variance, and its squared envelope follows an exponential distribution with one parameter, while the exact distribution parameter is undefined. The recently introduced log-envelope (i.e. the logarithm of the envelope) and generalized envelope (GE) exhibit attractive properties against interfering noise, however, their probability distributions have not yet been established. In this paper, the probability distributions of the squared envelope, log-squared envelope (i.e. the logarithm of the squared envelope), log-envelope and GE with parameter greater than 0 of Gaussian noise and corresponding distribution parameters are derived and established theoretically, and the important characteristic that their distribution parameters vary with the noise variance is clarified. On this basis, typical sparsity measures of GE of Gaussian noise are theoretically calculated, including kurtosis, skewness, Li/Lj norm, Hoyer measure, modified smoothness index, negentropy, Gini index, Gini index Ⅱ and Gini index Ⅲ. These typical sparsity measures of GE with parameter greater than 0 of Gaussian noise and the skewness and kurtosis of the log-envelope of Gaussian noise are proven to be independent of the noise variance, which enables them to serve as baselines for machine condition monitoring. Numerical simulations verify the correctness of the probability distributions and theoretical values of typical sparsity measures of GE with different parameters of Gaussian noise. The analysis results of four bearing run-to-failure experiments verify the feasibility and effectiveness of the sparsity measure of Gaussian noise as a condition monitoring baseline and demonstrate the efficacy and performance of GE-based sparsity measures for machine condition monitoring.
建立机器振动信号的概率分布对于计算机器健康指标的理论基线至关重要。基于包络和平方包络的健康指标是状态监测的一个重要系列。在良好机器振动信号为高斯分布的假设下，一个均值为零的正态机器信号的包络被证明遵循一个参数（取决于噪声方差）的瑞利（Rayleigh）分布，其平方包络遵循一个参数的指数分布，而确切的分布参数未定义。最近推出的对数包络（即包络的对数）和广义包络（GE）在抗干扰噪声方面表现出诱人的特性，但它们的概率分布尚未确定。本文从理论上推导并建立了参数大于 0 的高斯噪声的平方包络、对数平方包络（即平方包络的对数）、对数包络和广义包络的概率分布以及相应的分布参数，并阐明了它们的分布参数随噪声方差变化的重要特性。在此基础上，从理论上计算了高斯噪声 GE 的典型稀疏度量，包括峰度、偏度、Li/Lj 常模、霍耶度量、修正平滑指数、负熵、基尼指数、基尼指数Ⅱ和基尼指数Ⅲ。这些典型的稀疏度量证明了参数大于 0 的高斯噪声 GE 以及高斯噪声对数包络的偏度和峰度与噪声方差无关，因此可作为机器状态监测的基准。数值模拟验证了不同高斯噪声参数下 GE 典型稀疏度量的概率分布和理论值的正确性。四个轴承运行至故障实验的分析结果验证了高斯噪声稀疏度量作为状态监测基线的可行性和有效性，并证明了基于通用电气的稀疏度量在机器状态监测中的功效和性能。
{"title":"Probability distributions and typical sparsity measures of Hilbert transform-based generalized envelopes and their application to machine condition monitoring","authors":"Bingyan Chen , Wade A. Smith , Yao Cheng , Fengshou Gu , Fulei Chu , Weihua Zhang , Andrew D. Ball","doi":"10.1016/j.ymssp.2024.112026","DOIUrl":"10.1016/j.ymssp.2024.112026","url":null,"abstract":"<div><div>The establishment of probability distributions of machine vibration signals is crucial for calculating theoretical baselines of machine health indicators. Health indicators based on the envelope and squared envelope are an important family for condition monitoring. Under the assumption that the vibration signals of a good machine are Gaussian distributed, the envelope of a normal machine signal with zero mean is proven to follow a Rayleigh distribution with one parameter that depends on the noise variance, and its squared envelope follows an exponential distribution with one parameter, while the exact distribution parameter is undefined. The recently introduced log-envelope (i.e. the logarithm of the envelope) and generalized envelope (GE) exhibit attractive properties against interfering noise, however, their probability distributions have not yet been established. In this paper, the probability distributions of the squared envelope, log-squared envelope (i.e. the logarithm of the squared envelope), log-envelope and GE with parameter greater than 0 of Gaussian noise and corresponding distribution parameters are derived and established theoretically, and the important characteristic that their distribution parameters vary with the noise variance is clarified. On this basis, typical sparsity measures of GE of Gaussian noise are theoretically calculated, including kurtosis, skewness, <em>Li</em>/<em>Lj</em> norm, Hoyer measure, modified smoothness index, negentropy, Gini index, Gini index Ⅱ and Gini index Ⅲ. These typical sparsity measures of GE with parameter greater than 0 of Gaussian noise and the skewness and kurtosis of the log-envelope of Gaussian noise are proven to be independent of the noise variance, which enables them to serve as baselines for machine condition monitoring. Numerical simulations verify the correctness of the probability distributions and theoretical values of typical sparsity measures of GE with different parameters of Gaussian noise. The analysis results of four bearing run-to-failure experiments verify the feasibility and effectiveness of the sparsity measure of Gaussian noise as a condition monitoring baseline and demonstrate the efficacy and performance of GE-based sparsity measures for machine condition monitoring.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112026"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.112011
Yingrui Wu , Fei Kang , Gang Wan , Hongjun Li
Modal identification based on ambient vibration has gained increasing importance in monitoring the operational behavior of dams. This paper develops a robust automated modal identification method to identify the modal parameters of concrete dams. The proposed method requires no modal validation criteria beyond some widely used thresholds. Initially, the covariance-driven stochastic subspace identification (SSI-COV) algorithm is utilized to extract modal parameters, then introducing an improved stabilization diagram to eliminate spurious modes. Subsequently, a hybrid clustering algorithm that combines the clustering by fast search and find of density peaks (DPC) algorithm with a shared nearest neighbor approach is proposed to group physical modes. Clustering centers are determined automatically through a statistics-based method. Finally, the boxplot method is employed to detect and remove outliers from each cluster, thereby facilitating more accurate modal parameter estimation. The performance of the proposed method is validated by identifying the modal parameters of a five-degree-of-freedom frame model and a small-scale arch dam. The results demonstrate that the proposed method is capable of automatically identifying modal parameters with considerable accuracy and robustness.
{"title":"Automatic operational modal analysis for concrete arch dams integrating improved stabilization diagram with hybrid clustering algorithm","authors":"Yingrui Wu , Fei Kang , Gang Wan , Hongjun Li","doi":"10.1016/j.ymssp.2024.112011","DOIUrl":"10.1016/j.ymssp.2024.112011","url":null,"abstract":"<div><div>Modal identification based on ambient vibration has gained increasing importance in monitoring the operational behavior of dams. This paper develops a robust automated modal identification method to identify the modal parameters of concrete dams. The proposed method requires no modal validation criteria beyond some widely used thresholds. Initially, the covariance-driven stochastic subspace identification (SSI-COV) algorithm is utilized to extract modal parameters, then introducing an improved stabilization diagram to eliminate spurious modes. Subsequently, a hybrid clustering algorithm that combines the clustering by fast search and find of density peaks (DPC) algorithm with a shared nearest neighbor approach is proposed to group physical modes. Clustering centers are determined automatically through a statistics-based method. Finally, the boxplot method is employed to detect and remove outliers from each cluster, thereby facilitating more accurate modal parameter estimation. The performance of the proposed method is validated by identifying the modal parameters of a five-degree-of-freedom frame model and a small-scale arch dam. The results demonstrate that the proposed method is capable of automatically identifying modal parameters with considerable accuracy and robustness.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112011"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.111992
Luc S. Keizers , R. Loendersloot , T. Tinga
Unexpected system failures are costly and preventing them is crucial to guarantee availability and reliability of complex assets. Prognostics help to increase the availability and reliability. However, existing methods have their limitations: physics-based methods have limited adaptivity to specific applications, while data-driven methods heavily rely on (scarcely available) historical data, which reduces their prognostic performance. Especially when operational conditions change over time, existing methods do not always perform well. As a solution, this paper proposes a new framework in which loads are explicitly incorporated in a prognostic method based on Bayesian filtering. This is accomplished by zooming in on the failure mechanism on the material level, thus establishing a quantitative relation between usage and degradation rates. This relation is updated using a Bayesian filter and measured loads, but also allows accurate degradation predictions by considering future (changing) loads. This enables decision support on either operational use or maintenance activities. The performance of the proposed load-controlled prognostic method is demonstrated in an atmospheric corrosion use case, based on a public real data set constructed from annual corrosion measurements on carbon steel specimens. The developed load-controlled particle filter (LCPF) is demonstrated to outperform a method based on a regular particle filter, a regression model and an ARIMA model for this specific scenario with changing operating conditions. The generalization of the framework is demonstrated by two additional conceptual case studies on crack propagation and seal wear.
{"title":"Bayesian filtering based prognostic framework incorporating varying loads","authors":"Luc S. Keizers , R. Loendersloot , T. Tinga","doi":"10.1016/j.ymssp.2024.111992","DOIUrl":"10.1016/j.ymssp.2024.111992","url":null,"abstract":"<div><div>Unexpected system failures are costly and preventing them is crucial to guarantee availability and reliability of complex assets. Prognostics help to increase the availability and reliability. However, existing methods have their limitations: physics-based methods have limited adaptivity to specific applications, while data-driven methods heavily rely on (scarcely available) historical data, which reduces their prognostic performance. Especially when operational conditions change over time, existing methods do not always perform well. As a solution, this paper proposes a new framework in which loads are explicitly incorporated in a prognostic method based on Bayesian filtering. This is accomplished by zooming in on the failure mechanism on the material level, thus establishing a quantitative relation between usage and degradation rates. This relation is updated using a Bayesian filter and measured loads, but also allows accurate degradation predictions by considering future (changing) loads. This enables decision support on either operational use or maintenance activities. The performance of the proposed load-controlled prognostic method is demonstrated in an atmospheric corrosion use case, based on a public real data set constructed from annual corrosion measurements on carbon steel specimens. The developed load-controlled particle filter (LCPF) is demonstrated to outperform a method based on a regular particle filter, a regression model and an ARIMA model for this specific scenario with changing operating conditions. The generalization of the framework is demonstrated by two additional conceptual case studies on crack propagation and seal wear.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 111992"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.ymssp.2024.112023
Rujie Hou , Zhousuo Zhang , Jinglong Chen , Zheng Liu , Lixin Tu
Accurate interlayer slipping recognition in viscoelastic sandwich structures (VSSs) is critical for mechanical equipment’s safety and reliability. However, significant domain shifts exist in VSSs data under variable working conditions, and domain data under certain conditions cannot be directly accessed during training. This renders conventional domain adaptation methods ineffective. To address the problems, we proposed causality-augmented generalization network (CGN) without accessing target domains for VSSs’ slipping recognition. CGN comprises a swin-transformer feature extractor and a capsule network classifier with an FC decoder. The feature extractor aims to fully extract discriminative features of VSSs data and promote their domain invariance across multiple domains. Building on this foundation, the classifier further extracts the underlying causal features associated with the labels and performs slipping recognition, thereby enhancing the model’s generalization and stability across various domains. The decoder serves as a regularizer to assist in learning meaningful representations of input data. Moreover, cross-domain meta-learning strategy is incorporated into the generalized training process to further strengthen the model’s generalization ability. The experiments on VSSs’ cross-domain datasets illustrate that CGN can be trained on some domains and directly tested on multiple unknown domains with desirable results, showing its effective generalization and stability for slipping recognition.
{"title":"Causality-Augmented generalization network with cross-domain meta-learning for interlayer slipping recognition in viscoelastic sandwich structures","authors":"Rujie Hou , Zhousuo Zhang , Jinglong Chen , Zheng Liu , Lixin Tu","doi":"10.1016/j.ymssp.2024.112023","DOIUrl":"10.1016/j.ymssp.2024.112023","url":null,"abstract":"<div><div>Accurate interlayer slipping recognition in viscoelastic sandwich structures (VSSs) is critical for mechanical equipment’s safety and reliability. However, significant domain shifts exist in VSSs data under variable working conditions, and domain data under certain conditions cannot be directly accessed during training. This renders conventional domain adaptation methods ineffective. To address the problems, we proposed causality-augmented generalization network (CGN) without accessing target domains for VSSs’ slipping recognition. CGN comprises a swin-transformer feature extractor and a capsule network classifier with an FC decoder. The feature extractor aims to fully extract discriminative features of VSSs data and promote their domain invariance across multiple domains. Building on this foundation, the classifier further extracts the underlying causal features associated with the labels and performs slipping recognition, thereby enhancing the model’s generalization and stability across various domains. The decoder serves as a regularizer to assist in learning meaningful representations of input data. Moreover, cross-domain <em>meta</em>-learning strategy is incorporated into the generalized training process to further strengthen the model’s generalization ability. The experiments on VSSs’ cross-domain datasets illustrate that CGN can be trained on some domains and directly tested on multiple unknown domains with desirable results, showing its effective generalization and stability for slipping recognition.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112023"},"PeriodicalIF":7.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.ymssp.2024.111985
Marie Brøns
Many industrial applications apply non-slender bolts, from small bolts in machinery to large bolts in offshore structures. Ensuring the correct tension in such bolts is a significant problem. Recent work suggests a vibration-based approach for estimating bolt tension. The idea is to assume the bolt is an Euler–Bernoulli beam and measure the bending natural frequencies. When tightening the bolt, the frequencies increase. For non-slender bolts, the Euler–Bernoulli assumption is no longer valid. Therefore, a tensioned Timoshenko beam model with flexible boundary conditions is derived in this work. Derivation and investigation of a tensioned Timoshenko beam with boundary mass, inertia, and flexible boundary conditions is not well described in the literature. Besides the purpose of estimating tension, the investigation provides a fundamental understanding of how boundary conditions influence natural frequencies in the Timoshenko formulation, offering novel insights that may be useful in other applications. The Timoshenko model is incorporated into a previously applied parameter estimation method and validated by testing numerical scenarios of tightened bolts. Despite finding that non-slender bolts’ natural frequencies depend relatively less on tension than slender bolts, it is still possible to make estimations with an average deviation of less than 2%. Finally, to test that the Timoshenko model is a valid assumption, experiments are performed on a non-slender M72 bolt.
{"title":"Vibration-based estimation of bolt tension in non-slender bolts using Timoshenko beam theory","authors":"Marie Brøns","doi":"10.1016/j.ymssp.2024.111985","DOIUrl":"10.1016/j.ymssp.2024.111985","url":null,"abstract":"<div><div>Many industrial applications apply non-slender bolts, from small bolts in machinery to large bolts in offshore structures. Ensuring the correct tension in such bolts is a significant problem. Recent work suggests a vibration-based approach for estimating bolt tension. The idea is to assume the bolt is an Euler–Bernoulli beam and measure the bending natural frequencies. When tightening the bolt, the frequencies increase. For non-slender bolts, the Euler–Bernoulli assumption is no longer valid. Therefore, a tensioned Timoshenko beam model with flexible boundary conditions is derived in this work. Derivation and investigation of a tensioned Timoshenko beam with boundary mass, inertia, and flexible boundary conditions is not well described in the literature. Besides the purpose of estimating tension, the investigation provides a fundamental understanding of how boundary conditions influence natural frequencies in the Timoshenko formulation, offering novel insights that may be useful in other applications. The Timoshenko model is incorporated into a previously applied parameter estimation method and validated by testing numerical scenarios of tightened bolts. Despite finding that non-slender bolts’ natural frequencies depend relatively less on tension than slender bolts, it is still possible to make estimations with an average deviation of less than 2%. Finally, to test that the Timoshenko model is a valid assumption, experiments are performed on a non-slender M72 bolt.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 111985"},"PeriodicalIF":7.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.ymssp.2024.112018
Dingcheng Ji , Jing Lin , Fei Gao , Jiadong Hua , Wenhao Li
The structural integrity and safety of carbon fiber reinforced plastics (CFRP) are vulnerable to delamination, which is often imperceptible to the naked eye. Although the Scanning Laser Doppler Vibrometer (SLDV) has shown promise in damage quantification of CFRP, its time-consuming measurement process limits its application in engineering scenarios. To address this, we introduce a novel damage index, the spatial gradient, which captures the interaction between delamination and the wavefield. We have also developed a neural network capable of reconstructing the spatial gradient directly from high-sparsity Lamb wavefield data obtained at an extremely low spatial sampling rate, thereby significantly reducing measurement time. To enhance the network’s capability to detect wavefield anomalies, we employ the cross-attention technique, allowing for the direct injection of shallow features representing local wavefield distortions caused by damage into the decoder. Additionally, we integrate multiple reconstruction layers to guide the wavefield reconstruction process, ensuring meaningful information is captured at each stage. Our method achieves substantial improvements in reconstruction accuracy, increasing from 70 % to 92 % in single-damage scenario and from 14 % to 72 % in multi-damage scenario compared to the previous state-of-the-art techniques. By using the reconstructed spatial gradient field for damage imaging through spatial covariance analysis, our approach demonstrates its feasibility and generalizability across various damage locations. This suggests its potential as a reliable solution for fast and accurate damage characterization, reducing the measurement burden and enhancing practical applicability.
{"title":"A deep learning-based spatial gradient reconstruction method for efficient damage identification in composite with high-sparsity Lamb wavefield","authors":"Dingcheng Ji , Jing Lin , Fei Gao , Jiadong Hua , Wenhao Li","doi":"10.1016/j.ymssp.2024.112018","DOIUrl":"10.1016/j.ymssp.2024.112018","url":null,"abstract":"<div><div>The structural integrity and safety of carbon fiber reinforced plastics (CFRP) are vulnerable to delamination, which is often imperceptible to the naked eye. Although the Scanning Laser Doppler Vibrometer (SLDV) has shown promise in damage quantification of CFRP, its time-consuming measurement process limits its application in engineering scenarios. To address this, we introduce a novel damage index, the spatial gradient, which captures the interaction between delamination and the wavefield. We have also developed a neural network capable of reconstructing the spatial gradient directly from high-sparsity Lamb wavefield data obtained at an extremely low spatial sampling rate, thereby significantly reducing measurement time. To enhance the network’s capability to detect wavefield anomalies, we employ the cross-attention technique, allowing for the direct injection of shallow features representing local wavefield distortions caused by damage into the decoder. Additionally, we integrate multiple reconstruction layers to guide the wavefield reconstruction process, ensuring meaningful information is captured at each stage. Our method achieves substantial improvements in reconstruction accuracy, increasing from 70 % to 92 % in single-damage scenario and from 14 % to 72 % in multi-damage scenario compared to the previous state-of-the-art techniques. By using the reconstructed spatial gradient field for damage imaging through spatial covariance analysis, our approach demonstrates its feasibility and generalizability across various damage locations. This suggests its potential as a reliable solution for fast and accurate damage characterization, reducing the measurement burden and enhancing practical applicability.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"224 ","pages":"Article 112018"},"PeriodicalIF":7.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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