C. Lindley, T. Rogers, R. Dwyer-Joyce, N. Dervilis, K. Worden
In structural health monitoring (SHM) and condition monitoring (CM) applications, the expense of testing programmes may be too high to obtain adequate datasets. When limited by the number of available data samples, one may rely on dimensional reduction methods to proceed with a meaningful statistical and probabilistic analysis. In this work, some state-of-the-art dimensionality-reduction techniques were investigated as part of a simple ball-bearing damage detection problem. A variational auto-encoder (VAE) was compared to other methods, based on their capability to generate low-dimensional representations of the data. Unlike other common alternatives, such as principal component analysis (PCA) or auto-encoding (AE) networks, the VAE introduces a probabilistic framework via the latent embeddings. A well-defined distribution is thereby constructed on the latent variables, making the transformed dataset an optimal one for subsequent pattern recognition analysis. The results demonstrated an increase in classification performance given the low-dimensional representation generated by the VAE.
{"title":"ON THE APPLICATION OF VARIATIONAL AUTO ENCODERS (VAE) FOR DAMAGE DETECTION IN ROLLING ELEMENT BEARINGS","authors":"C. Lindley, T. Rogers, R. Dwyer-Joyce, N. Dervilis, K. Worden","doi":"10.12783/shm2021/36281","DOIUrl":"https://doi.org/10.12783/shm2021/36281","url":null,"abstract":"In structural health monitoring (SHM) and condition monitoring (CM) applications, the expense of testing programmes may be too high to obtain adequate datasets. When limited by the number of available data samples, one may rely on dimensional reduction methods to proceed with a meaningful statistical and probabilistic analysis. In this work, some state-of-the-art dimensionality-reduction techniques were investigated as part of a simple ball-bearing damage detection problem. A variational auto-encoder (VAE) was compared to other methods, based on their capability to generate low-dimensional representations of the data. Unlike other common alternatives, such as principal component analysis (PCA) or auto-encoding (AE) networks, the VAE introduces a probabilistic framework via the latent embeddings. A well-defined distribution is thereby constructed on the latent variables, making the transformed dataset an optimal one for subsequent pattern recognition analysis. The results demonstrated an increase in classification performance given the low-dimensional representation generated by the VAE.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114255689","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}
Mahyar Kazemian, Sajad Nikdel, Mehrnaz Mohammadesmaeili, V. Nik, K. Zandi
Environmental loads, such as wind and river flow, play an essential role in the structural design and structural assessment of long-span bridges. Climate change and extreme climatic events are threats to the reliability and safety of the transport network. This has led to a growing demand for digital twin models to investigate the resilience of bridges under extreme climate conditions. Kalix bridge, constructed over the Kalix river in Sweden in 1956, is used as a testbed in this context. The bridge structure, made of posttensioned concrete, consists of five spans, with the longest one being 94 m. In this study, aerodynamic characteristics and extreme values of numerical wind simulation such as surface pressure are obtained by using Spalart-Allmaras Delayed Detached Eddy Simulation (DDES) as a hybrid RANS-LES turbulence approach which is both practical and computationally efficient for near-wall mesh density imposed by the LES method. Surface wind pressure is obtained for three extreme climate scenarios, including extreme windy weather, extremely cold weather, and design value for a 3000-year return period. The result indicates significant differences in surface wind pressure due to time layers coming from transient wind flow simulation. In order to assess the structural performance under the critical wind scenario, the highest value of surface pressure for each scenario is considered. Also, a hydrodynamic study is conducted on the bridge pillars, in which the river flow is simulated using the VOF method, and the water movement process around the pillars is examined transiently and at different times. The surface pressure applied by the river flow with the highest recorded volumetric flow is calculated on each of the pier surfaces. In simulating the river flow, information and weather conditions recorded in the past periods have been used. The results show that the surface pressure at the time when the river flow hit the pillars is much higher than in subsequent times. This amount of pressure can be used as a critical load in fluid-structure interaction (FSI) calculations. Finally, for both sections, the wind surface pressure, the velocity field with respect to auxiliary probe lines, the water circumferential motion contours around the pillars, and the pressure diagram on them are reported in different timesteps.
{"title":"KALIX BRIDGE DIGITAL TWIN—STRUCTURAL LOADS FROM FUTURE EXTREME CLIMATE EVENTS","authors":"Mahyar Kazemian, Sajad Nikdel, Mehrnaz Mohammadesmaeili, V. Nik, K. Zandi","doi":"10.12783/shm2021/36323","DOIUrl":"https://doi.org/10.12783/shm2021/36323","url":null,"abstract":"Environmental loads, such as wind and river flow, play an essential role in the structural design and structural assessment of long-span bridges. Climate change and extreme climatic events are threats to the reliability and safety of the transport network. This has led to a growing demand for digital twin models to investigate the resilience of bridges under extreme climate conditions. Kalix bridge, constructed over the Kalix river in Sweden in 1956, is used as a testbed in this context. The bridge structure, made of posttensioned concrete, consists of five spans, with the longest one being 94 m. In this study, aerodynamic characteristics and extreme values of numerical wind simulation such as surface pressure are obtained by using Spalart-Allmaras Delayed Detached Eddy Simulation (DDES) as a hybrid RANS-LES turbulence approach which is both practical and computationally efficient for near-wall mesh density imposed by the LES method. Surface wind pressure is obtained for three extreme climate scenarios, including extreme windy weather, extremely cold weather, and design value for a 3000-year return period. The result indicates significant differences in surface wind pressure due to time layers coming from transient wind flow simulation. In order to assess the structural performance under the critical wind scenario, the highest value of surface pressure for each scenario is considered. Also, a hydrodynamic study is conducted on the bridge pillars, in which the river flow is simulated using the VOF method, and the water movement process around the pillars is examined transiently and at different times. The surface pressure applied by the river flow with the highest recorded volumetric flow is calculated on each of the pier surfaces. In simulating the river flow, information and weather conditions recorded in the past periods have been used. The results show that the surface pressure at the time when the river flow hit the pillars is much higher than in subsequent times. This amount of pressure can be used as a critical load in fluid-structure interaction (FSI) calculations. Finally, for both sections, the wind surface pressure, the velocity field with respect to auxiliary probe lines, the water circumferential motion contours around the pillars, and the pressure diagram on them are reported in different timesteps.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908133","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}
Antoni Lis, Micah Sweeney, M. Samotyj, Artur ARTUR HANC
Machinery monitoring is typically applied to a single machine based on sensor integration and data analysis. Such an approach to a set of machines operating in similar conditions allows for a multivariate analysis for condition monitoring based on a single machine as well as based on group analysis. This paper describes an Industrial Internet-of-Thing (IIoT) concept for condition monitoring of machinery population based on water pumps. The first part provides an introduction to unsupervised anomaly detection based on population modeling with using features calculated from the: mechanical (based on vibration sensors), electrical (voltage and current signals collected from electric motors that drive monitored pumps) and operational processes (such as pressures, flows) signals. Finally, the preliminary results from laboratory testing and demonstration at a wastewater processing plant are presented.
{"title":"POPULATION BASED PUMPS MONITORING AND BENCHMARKING USING IOT AND EDGE ML LEARNING METHODS","authors":"Antoni Lis, Micah Sweeney, M. Samotyj, Artur ARTUR HANC","doi":"10.12783/shm2021/36283","DOIUrl":"https://doi.org/10.12783/shm2021/36283","url":null,"abstract":"Machinery monitoring is typically applied to a single machine based on sensor integration and data analysis. Such an approach to a set of machines operating in similar conditions allows for a multivariate analysis for condition monitoring based on a single machine as well as based on group analysis. This paper describes an Industrial Internet-of-Thing (IIoT) concept for condition monitoring of machinery population based on water pumps. The first part provides an introduction to unsupervised anomaly detection based on population modeling with using features calculated from the: mechanical (based on vibration sensors), electrical (voltage and current signals collected from electric motors that drive monitored pumps) and operational processes (such as pressures, flows) signals. Finally, the preliminary results from laboratory testing and demonstration at a wastewater processing plant are presented.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126995366","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}
Yujin Park, Yingjun Zhao Dubuc, Amy Slider, P. Sessoms, J. Fraser, K. Loh
Lateral ankle sprains cost billions of dollars in medical expenses annually and frequently result in long-term functional decline and a diminished health-related quality of life. While ankle braces have been shown to be effective in prophylaxis of subsequent ankle sprains, current braces are either too stiff and affect normal gait or too flexible and provide insufficient support during high-intensity activities. In this study, we proposed an adaptive ankle brace design that employs dynamically variable stiffness components to provide minimum support under normal gait movements and maximum rigidity under large ranges of motion. To achieve these unique properties, a honeycomb geometry was designed and three dimensionally printed with thermoplastic polyurethane to exhibit nonlinear, strain-stiffening, elastic behavior. We conducted a series of tensile load tests on different honeycomb unit cell configurations. First, the influence of unit cell designs on their mechanical strength and force-strain profiles was characterized. Second, experimentally calibrated finite element models of individual components simulated the mechanical response of the geometry, which were then used to optimize the geometrical parameters of the honeycomb shape (i.e., ring size, length of lateral elements, and thickness). The results identified promising design parameters for these honeycomb geometries that could be used to realize next-generation adaptive ankle braces.
{"title":"VARIABLE STIFFNESS HONEYCOMB METAMATERIALS FOR ADAPTIVE ANKLE BRACE DESIGN","authors":"Yujin Park, Yingjun Zhao Dubuc, Amy Slider, P. Sessoms, J. Fraser, K. Loh","doi":"10.12783/shm2021/36268","DOIUrl":"https://doi.org/10.12783/shm2021/36268","url":null,"abstract":"Lateral ankle sprains cost billions of dollars in medical expenses annually and frequently result in long-term functional decline and a diminished health-related quality of life. While ankle braces have been shown to be effective in prophylaxis of subsequent ankle sprains, current braces are either too stiff and affect normal gait or too flexible and provide insufficient support during high-intensity activities. In this study, we proposed an adaptive ankle brace design that employs dynamically variable stiffness components to provide minimum support under normal gait movements and maximum rigidity under large ranges of motion. To achieve these unique properties, a honeycomb geometry was designed and three dimensionally printed with thermoplastic polyurethane to exhibit nonlinear, strain-stiffening, elastic behavior. We conducted a series of tensile load tests on different honeycomb unit cell configurations. First, the influence of unit cell designs on their mechanical strength and force-strain profiles was characterized. Second, experimentally calibrated finite element models of individual components simulated the mechanical response of the geometry, which were then used to optimize the geometrical parameters of the honeycomb shape (i.e., ring size, length of lateral elements, and thickness). The results identified promising design parameters for these honeycomb geometries that could be used to realize next-generation adaptive ankle braces.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127641550","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}
The initiation and development of microcracks introduced by heating or fire plays a critical role in the stability and durability of concrete structure. However, the concealment of microcracks and the nonlinearity of concrete materials make it difficult to appropriately evaluate the size and extension state of microcracks inside the thermal damaged concrete. In this paper, broadband frequency excitation instead of traditional dual-frequency excitation is utilized to excite the thermal damaged concrete, and the generated ultrasonic modulated signal reflects the micro damage state. The concept of damage index (DI) based on the sideband peak count (SPC) is proposed to quantitatively describe the variation characteristics of modulated signals. The results show that the peak value of DI based on broadband frequency coupling of nonlinear ultrasonic modulation method reflects the generation and development of microcracks in thermal damaged concrete. The peak value of DI increases sensitively with the increasing of water-cement ratio, fine-coarse aggregate ratio, and the heating temperature. Meanwhile, the statistical relationships of the peak value of DI with the residual strength and the area ratio of microcracks in thermal damaged concrete are established respectively.
{"title":"MICROCRACKS DETECTION OF THERMAL DAMAGED CONCRETE WITH NONLINEAR ULTRASONIC MODULATION BASED ON BROAD BAND FREQUENCY COUPLING","authors":"Ying Xu, Heyong Zhang, Huan Liu","doi":"10.12783/shm2021/36360","DOIUrl":"https://doi.org/10.12783/shm2021/36360","url":null,"abstract":"The initiation and development of microcracks introduced by heating or fire plays a critical role in the stability and durability of concrete structure. However, the concealment of microcracks and the nonlinearity of concrete materials make it difficult to appropriately evaluate the size and extension state of microcracks inside the thermal damaged concrete. In this paper, broadband frequency excitation instead of traditional dual-frequency excitation is utilized to excite the thermal damaged concrete, and the generated ultrasonic modulated signal reflects the micro damage state. The concept of damage index (DI) based on the sideband peak count (SPC) is proposed to quantitatively describe the variation characteristics of modulated signals. The results show that the peak value of DI based on broadband frequency coupling of nonlinear ultrasonic modulation method reflects the generation and development of microcracks in thermal damaged concrete. The peak value of DI increases sensitively with the increasing of water-cement ratio, fine-coarse aggregate ratio, and the heating temperature. Meanwhile, the statistical relationships of the peak value of DI with the residual strength and the area ratio of microcracks in thermal damaged concrete are established respectively.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133256390","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}
Monitoring the behavior and performance of engineered structures has become increasingly desirable due to the value such information offers for occupant safety and structural maintenance. Vibration data collected from accelerometers has proven to be an effective tool to perform this type of monitoring. While some monitoring activities can occur autonomously, it is often necessary for humans to interact with the data to discern the need for additional evaluation. In large structures or those with a dense sensor deployment, continuously collected vibration data can quickly grow to massive scales. Consequently, the evaluation of structural performance is often limited by the ability of a system to efficiently process and present large volumes of data. To overcome this challenge, this paper presents a framework to process, store, and visualize data using open-source distributed computing technologies. The framework utilizes a publish-subscribe messaging queue deployed across multiple partitions to consume data in parallel, improving the rate of ingestion. Ingested data is stored in a structured format using a NoSQL database that provides high availability, scalability, and performance. The stored data acts as the source for webbased visualization. This setup provides a high degree of adaptability, allowing meaningful visualizations to be implemented for various forms of smart infrastructure monitoring tasks. The capabilities of the resultant human-infrastructure interface are demonstrated using Goodwin Hall, a five-story building instrumented with 225 hard-wired accelerometers. This case study showcases visualizations that enable users to perform real-time assessment of frequency domain features and efficiently identify notable excitation events during the building's history.
{"title":"A HIGH-VOLUME PROCESSING FRAMEWORK FOR HUMAN-STRUCTURE INTERFACES IN SMART INFRASTRUCTURE","authors":"Natasha Vipond, Abhinav Kumar, Zhiwu Xie, Rodrigo Sarlo","doi":"10.12783/shm2021/36252","DOIUrl":"https://doi.org/10.12783/shm2021/36252","url":null,"abstract":"Monitoring the behavior and performance of engineered structures has become increasingly desirable due to the value such information offers for occupant safety and structural maintenance. Vibration data collected from accelerometers has proven to be an effective tool to perform this type of monitoring. While some monitoring activities can occur autonomously, it is often necessary for humans to interact with the data to discern the need for additional evaluation. In large structures or those with a dense sensor deployment, continuously collected vibration data can quickly grow to massive scales. Consequently, the evaluation of structural performance is often limited by the ability of a system to efficiently process and present large volumes of data. To overcome this challenge, this paper presents a framework to process, store, and visualize data using open-source distributed computing technologies. The framework utilizes a publish-subscribe messaging queue deployed across multiple partitions to consume data in parallel, improving the rate of ingestion. Ingested data is stored in a structured format using a NoSQL database that provides high availability, scalability, and performance. The stored data acts as the source for webbased visualization. This setup provides a high degree of adaptability, allowing meaningful visualizations to be implemented for various forms of smart infrastructure monitoring tasks. The capabilities of the resultant human-infrastructure interface are demonstrated using Goodwin Hall, a five-story building instrumented with 225 hard-wired accelerometers. This case study showcases visualizations that enable users to perform real-time assessment of frequency domain features and efficiently identify notable excitation events during the building's history.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":" 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132187480","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}
L. Lomazzi, Á. González-Jiménez, F. Cadini, A. Manes, M. Giglio
A common active structural health monitoring (SHM) solution for thin-walled structures consists of processing ultrasonic guided waves signals, which are excited and sensed by means of a network of piezoelectric devices installed on the structure, with the purpose of providing a tomographic reconstruction-based damage probability map of the structure. A promising reconstruction algorithm typically employed within this framework is the Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) algorithm, which has been shown to provide satisfactory results in terms of damage detection and localisation. However, this algorithm comes with some disadvantages and minor issues, such as artefacts creation in case an unevenly distributed sensors layout is installed on the structure, which may significantly worsen the damage diagnosis performance of the monitoring framework. In this paper, an enhancement of the original RAPID algorithm is presented, which exploits spatial filtering techniques to reduce possible artificially created artefacts, thus allowing installing on structures any network of sensors without reducing the diagnostic performances. The improved damage localisation accuracy obtained using the proposed algorithm is proven by means of a case study involving a numerical model of a realistic composite panel with an unevenly distributed network of sensors.
{"title":"SPATIAL FILTERING TECHNIQUE-BASED ENHANCEMENT OF THE RECONSTRUCTION ALGORITHM FOR THE PROBABILISTIC INSPECTION OF DAMAGE (RAPID)","authors":"L. Lomazzi, Á. González-Jiménez, F. Cadini, A. Manes, M. Giglio","doi":"10.12783/shm2021/36314","DOIUrl":"https://doi.org/10.12783/shm2021/36314","url":null,"abstract":"A common active structural health monitoring (SHM) solution for thin-walled structures consists of processing ultrasonic guided waves signals, which are excited and sensed by means of a network of piezoelectric devices installed on the structure, with the purpose of providing a tomographic reconstruction-based damage probability map of the structure. A promising reconstruction algorithm typically employed within this framework is the Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID) algorithm, which has been shown to provide satisfactory results in terms of damage detection and localisation. However, this algorithm comes with some disadvantages and minor issues, such as artefacts creation in case an unevenly distributed sensors layout is installed on the structure, which may significantly worsen the damage diagnosis performance of the monitoring framework. In this paper, an enhancement of the original RAPID algorithm is presented, which exploits spatial filtering techniques to reduce possible artificially created artefacts, thus allowing installing on structures any network of sensors without reducing the diagnostic performances. The improved damage localisation accuracy obtained using the proposed algorithm is proven by means of a case study involving a numerical model of a realistic composite panel with an unevenly distributed network of sensors.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114193115","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}
Osseointegrated prostheses are widely used as the treatment for femur amputation. However, this technique requires sufficient implant stability before and during the rehabilitation period to mitigate the risk of implant breakage and loosening. Hence, reliable assessment methods for the osseointegration process are essential to ensure initial and long-term implant stability. This paper aims to investigate a vibration analysis method with a novel implant design, which focuses on the analysis of the dynamic response of the femur-implant system during the simulated osseointegration process. The paper also proposes a concept of using normalized energy difference to formulate an energy index (E-index). A 133mm-long amputated artificial femur model was constrained at the proximal end with a customized clamp. The epoxy adhesives were applied at the interface between the aforementioned femur and implant to simulate the change in stiffness in mimicking the osseointegration process. A two-unidirectionalsensor setup attached to the bottom of the implant was used to record the dynamic response stimulated by an impact hammer. The results show a significant change in magnitude of the cross-spectrum during the osseointegration processes. The resonance modes in cross-spectrum for the frequency above 1000Hz are hard to distinguish suggested that the vibration of the system being hindered by the high dampening effect of the adhesive before the initial bonding of the adhesive at 300s. The plot of E-index shows a clear correlation that the E-index provided a potential quantitative approach for monitoring the stages of osseointegration. These findings highlight the feasibility of using the vibration analysis technique and E-index to quantitatively monitor the osseointegration process for future improvement on the efficiency of human health monitoring and patient rehabilitation.
{"title":"EXPERIMENTAL INVESTIGATION ON A NOVEL OSSEOINTEGRATED IMPLANT STABILITY ASSESSMENT USING ON VIBRATION ANALYSIS","authors":"S. Lu, B. Vien, M. Russ, M. Fitzgerald, W. Chiu","doi":"10.12783/shm2021/36348","DOIUrl":"https://doi.org/10.12783/shm2021/36348","url":null,"abstract":"Osseointegrated prostheses are widely used as the treatment for femur amputation. However, this technique requires sufficient implant stability before and during the rehabilitation period to mitigate the risk of implant breakage and loosening. Hence, reliable assessment methods for the osseointegration process are essential to ensure initial and long-term implant stability. This paper aims to investigate a vibration analysis method with a novel implant design, which focuses on the analysis of the dynamic response of the femur-implant system during the simulated osseointegration process. The paper also proposes a concept of using normalized energy difference to formulate an energy index (E-index). A 133mm-long amputated artificial femur model was constrained at the proximal end with a customized clamp. The epoxy adhesives were applied at the interface between the aforementioned femur and implant to simulate the change in stiffness in mimicking the osseointegration process. A two-unidirectionalsensor setup attached to the bottom of the implant was used to record the dynamic response stimulated by an impact hammer. The results show a significant change in magnitude of the cross-spectrum during the osseointegration processes. The resonance modes in cross-spectrum for the frequency above 1000Hz are hard to distinguish suggested that the vibration of the system being hindered by the high dampening effect of the adhesive before the initial bonding of the adhesive at 300s. The plot of E-index shows a clear correlation that the E-index provided a potential quantitative approach for monitoring the stages of osseointegration. These findings highlight the feasibility of using the vibration analysis technique and E-index to quantitatively monitor the osseointegration process for future improvement on the efficiency of human health monitoring and patient rehabilitation.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114866232","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}
A.A.R. Broer, Nan Yue, G. Galanopoulos, R. Benedictus, T. Loutas, D. Zarouchas
Health management methodologies for condition-based maintenance are often developed using sensor data collected during experimental tests. Most tests performed in laboratories focus on a coupon level or flat panels, while structural component testing is less commonly seen. As researchers, we often consider our experimental tests to be representative of a structure in a final application and consider the developed methodologies to be transferrable to these real-life structures. Yet, structures in their final applications such as wind turbines or aircraft are often larger, more complex, might contain various assembly details, and are loaded in complex conditions. These factors might influence the performance of developed diagnostic and prognostic methodologies and should therefore not be ignored. In our work, we consider the aspects of upscaling structural health monitoring (SHM) methodologies for stiffened composite panels with the design of the panels inspired by an aircraft wing structure. For this, we examine two levels of panels, namely a single- and multi-stiffener composite panel, where we consider the single-stiffener panel to be a representative lower-level version of the multi-stiffener panel. Multiple SHM sensors (acoustic emission, Lamb waves, strain sensing) were installed on both composite panels to monitor damage propagation during testing. We identify and analyse challenges and further discuss considerations that must be taken during upscaling of diagnostics and prognostics, and with that, aid in the development of health management methodologies for condition-based maintenance.
{"title":"ON THE CHALLENGES OF UPSCALING DAMAGE MONITORING METHODOLOGIES FOR STIFFENED COMPOSITE AIRCRAFT PANELS","authors":"A.A.R. Broer, Nan Yue, G. Galanopoulos, R. Benedictus, T. Loutas, D. Zarouchas","doi":"10.12783/shm2021/36237","DOIUrl":"https://doi.org/10.12783/shm2021/36237","url":null,"abstract":"Health management methodologies for condition-based maintenance are often developed using sensor data collected during experimental tests. Most tests performed in laboratories focus on a coupon level or flat panels, while structural component testing is less commonly seen. As researchers, we often consider our experimental tests to be representative of a structure in a final application and consider the developed methodologies to be transferrable to these real-life structures. Yet, structures in their final applications such as wind turbines or aircraft are often larger, more complex, might contain various assembly details, and are loaded in complex conditions. These factors might influence the performance of developed diagnostic and prognostic methodologies and should therefore not be ignored. In our work, we consider the aspects of upscaling structural health monitoring (SHM) methodologies for stiffened composite panels with the design of the panels inspired by an aircraft wing structure. For this, we examine two levels of panels, namely a single- and multi-stiffener composite panel, where we consider the single-stiffener panel to be a representative lower-level version of the multi-stiffener panel. Multiple SHM sensors (acoustic emission, Lamb waves, strain sensing) were installed on both composite panels to monitor damage propagation during testing. We identify and analyse challenges and further discuss considerations that must be taken during upscaling of diagnostics and prognostics, and with that, aid in the development of health management methodologies for condition-based maintenance.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124729723","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}
Chen Liu, O. Nagler, F. Tremmel, M. Unterreitmeier, Jessica J. Frick, D. Senesky
This investigation utilizes a material testing system that integrates acoustic emission (AE) testing with a nanoindentation system for crack generation and detection in Al-Cu top thin-film stack structures. The suitability of using the AE method was verified with scanning electron microscope (SEM) images of indent cross-sections. In order to cluster the AE signals based on a different physical meaning, a signal processing approach based on the Gaussian mixture model (GMM) clustering algorithm was applied. Principal component analysis (PCA) and autoencoder feature extraction methods were used to reduce the dimension of the signal. This signal processing approach has the promising ability to distinguish AE events associated with crack formation and metal layer plastic deformation. This integrated test system and signal processing approach provide a high-resolution mechanical testing platform for studying and enabling automatic, non-destructive crack detection in wafer probing.
{"title":"ACOUSTIC EMISSION SIGNAL PROCESSING STUDY OF NANOINDENTATION ON THIN FILM STACK STRUCTURES USING GAUSSIAN MIXTURE MODEL","authors":"Chen Liu, O. Nagler, F. Tremmel, M. Unterreitmeier, Jessica J. Frick, D. Senesky","doi":"10.12783/shm2021/36364","DOIUrl":"https://doi.org/10.12783/shm2021/36364","url":null,"abstract":"This investigation utilizes a material testing system that integrates acoustic emission (AE) testing with a nanoindentation system for crack generation and detection in Al-Cu top thin-film stack structures. The suitability of using the AE method was verified with scanning electron microscope (SEM) images of indent cross-sections. In order to cluster the AE signals based on a different physical meaning, a signal processing approach based on the Gaussian mixture model (GMM) clustering algorithm was applied. Principal component analysis (PCA) and autoencoder feature extraction methods were used to reduce the dimension of the signal. This signal processing approach has the promising ability to distinguish AE events associated with crack formation and metal layer plastic deformation. This integrated test system and signal processing approach provide a high-resolution mechanical testing platform for studying and enabling automatic, non-destructive crack detection in wafer probing.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434738","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}
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