L. Fazzi, N. Dias, M. Hołyńska, A. Tighe, R. Rampini, R. Groves
This research demonstrates the promising abilities of a tilted Fibre Bragg Grating (TFBG) sensor for monitoring the status of a silicone adhesive during a simulated space environment exposure. The silicone is used as adhesive between two thin cover glasses and the TFBG is embedded into the polymer such that it is fully enclosed. Then, the sample is exposed to standard space environment conditions in a vacuum chamber simulated by creating a high vacuum (1.3×10-6 mbar) and thermal cycles between -120 ℃ to 190 ℃. The TFBG spectra recorded during the exposure were demodulated to obtain the wavelength shifts of the Bragg and Ghost peaks and the envelope area of the upper and lower cladding modes resonances peaks. This will allow the thermomechanical and the refractive index (RI) variations of the silicone to be measured during the testing. In particular, the silicone RI depends on the material chemical and physical state and its thermal history, and the TFBG envelope area is sensitive to these RI changes. Hence, the envelope area of the TFBG spectrum can be used to obtain information on the evolution of the silicone adhesive during the test. The resulting trend of the selected peak wavelengths variation and envelope area were used to detect a variation of the degradation state of the material.
{"title":"MONITORING OF THERMAL AGEING CYCLES OF A SILICONE ADHESIVE IN A SIMULATED SPACE ENVIRONMENT USING EMBEDDED TFBG SENSORS","authors":"L. Fazzi, N. Dias, M. Hołyńska, A. Tighe, R. Rampini, R. Groves","doi":"10.12783/shm2021/36351","DOIUrl":"https://doi.org/10.12783/shm2021/36351","url":null,"abstract":"This research demonstrates the promising abilities of a tilted Fibre Bragg Grating (TFBG) sensor for monitoring the status of a silicone adhesive during a simulated space environment exposure. The silicone is used as adhesive between two thin cover glasses and the TFBG is embedded into the polymer such that it is fully enclosed. Then, the sample is exposed to standard space environment conditions in a vacuum chamber simulated by creating a high vacuum (1.3×10-6 mbar) and thermal cycles between -120 ℃ to 190 ℃. The TFBG spectra recorded during the exposure were demodulated to obtain the wavelength shifts of the Bragg and Ghost peaks and the envelope area of the upper and lower cladding modes resonances peaks. This will allow the thermomechanical and the refractive index (RI) variations of the silicone to be measured during the testing. In particular, the silicone RI depends on the material chemical and physical state and its thermal history, and the TFBG envelope area is sensitive to these RI changes. Hence, the envelope area of the TFBG spectrum can be used to obtain information on the evolution of the silicone adhesive during the test. The resulting trend of the selected peak wavelengths variation and envelope area were used to detect a variation of the degradation state of the material.","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":"132517692","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}
Sam Choppala, Poojhita Vurturbadarinath, M. Chierichetti, Fatemeh Davoudi Khaki
Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. This problem is particularly relevant in the development of autonomous vehicles, especially in the concept of urban air mobility. The actual usage of the vehicle will be used to predict stresses in the structure and therefore to define maintenance scheduling. Supervised regression machine learning algorithms are used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system, therefore creating a surrogate of the finite element model. The paper will present applications of the approach to a one-dimensional beam structure, modeled with finite element methods. Based on the response of the beam measured at a few reference locations, the surrogate finite element approach determines the entire response of the beam at all spatial locations (displacements, velocities, accelerations, stresses, strains) using neural networks. The FEA-based machine learning approach estimates the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe and efficient maintenance procedures. The effect of type of input features and output and their relationship on the performance of the neural network is discussed, as well as the effect of the beam boundary conditions on network performance.
{"title":"APPLICATIONS OF SURROGATE FINITE ELEMENT MACHINE LEARNING APPROACH FOR STRUCTURAL MONITORING","authors":"Sam Choppala, Poojhita Vurturbadarinath, M. Chierichetti, Fatemeh Davoudi Khaki","doi":"10.12783/shm2021/36284","DOIUrl":"https://doi.org/10.12783/shm2021/36284","url":null,"abstract":"Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. This problem is particularly relevant in the development of autonomous vehicles, especially in the concept of urban air mobility. The actual usage of the vehicle will be used to predict stresses in the structure and therefore to define maintenance scheduling. Supervised regression machine learning algorithms are used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system, therefore creating a surrogate of the finite element model. The paper will present applications of the approach to a one-dimensional beam structure, modeled with finite element methods. Based on the response of the beam measured at a few reference locations, the surrogate finite element approach determines the entire response of the beam at all spatial locations (displacements, velocities, accelerations, stresses, strains) using neural networks. The FEA-based machine learning approach estimates the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe and efficient maintenance procedures. The effect of type of input features and output and their relationship on the performance of the neural network is discussed, as well as the effect of the beam boundary conditions on network performance.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"24 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":"123557188","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, in order to efficiently distinguish the influence of both interface debonding defect and the mesoscale structure of concrete core on the stress wave field and the response of an embedded Piezoelectric-lead-zirconate-titanate (PZT) sensor in rectangular concrete filled steel tube (RCFST) members, a two dimensional (2D) mesoscale numerical concrete with homogenization approach considering the random distribution of circle, ellipse and irregular polygon aggregates is proposed firstly. Then, mesoscale homogenization simulation on stress wave fields within the cross-sections of RCFST members with and without interface debonding defects are carried out, respectively. The effect of both mesoscale structure of concrete core and the interface debonding defect on the stress wave field of each member is discussed. Therefore, the time-domain response of the embedded PZT sensor in the RCFST members coupled with PZT patches under sweep frequency excitation signal is determined and compared when both mesoscale models and their homogenization models are used. The sensitivity of the wavelet packet energy of the embedded PZT sensor response on the variation of both mesoscale structure of concrete core and the dimension of interface debonding defects is investigated. The detectability of interface debonding using stress wave measurement is illustrated efficiently with the proposed mesoscale homogenization modelling approach even the mesoscale structure of the concrete core is considered.
{"title":"MESOSCALE HOMOGENIZATION NUMERICAL STUDY ON THE SIGNIFICANCE OF CONCRETE MESOSCALE STRUCTURE ON WAVE PROPAGATION OF RECTANGULAR RCFSTS WITH DEBONDING","authors":"Jiang Wang, Bing-Lei Xu, Hongbing Chen, H. Ge","doi":"10.12783/shm2021/36319","DOIUrl":"https://doi.org/10.12783/shm2021/36319","url":null,"abstract":"In this paper, in order to efficiently distinguish the influence of both interface debonding defect and the mesoscale structure of concrete core on the stress wave field and the response of an embedded Piezoelectric-lead-zirconate-titanate (PZT) sensor in rectangular concrete filled steel tube (RCFST) members, a two dimensional (2D) mesoscale numerical concrete with homogenization approach considering the random distribution of circle, ellipse and irregular polygon aggregates is proposed firstly. Then, mesoscale homogenization simulation on stress wave fields within the cross-sections of RCFST members with and without interface debonding defects are carried out, respectively. The effect of both mesoscale structure of concrete core and the interface debonding defect on the stress wave field of each member is discussed. Therefore, the time-domain response of the embedded PZT sensor in the RCFST members coupled with PZT patches under sweep frequency excitation signal is determined and compared when both mesoscale models and their homogenization models are used. The sensitivity of the wavelet packet energy of the embedded PZT sensor response on the variation of both mesoscale structure of concrete core and the dimension of interface debonding defects is investigated. The detectability of interface debonding using stress wave measurement is illustrated efficiently with the proposed mesoscale homogenization modelling approach even the mesoscale structure of the concrete core is considered.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"31 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":"125033914","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}
Traditionally, dynamical systems can be simulated with physics-based model when the design parameters and material property are pre-known. However, when a system is deployed in field and has suffered potential degradation, a physics-based model might be infeasible to obtain. Moreover, the non-linearity and unknown coupling between the system and contacting constraints are often hard to determine accurately. The analysis of those systems becomes practically problematic. In this paper, the Koopman operator is used to learn and represent a dynamic system in a data driven manner. This paper proposes two methods of using the Koopman operator to extract and classify critical parameters of a non-linear dynamic mechanical system for fault diagnosis. The first method proposes a model to extract key features from a dynamic system and feed the features to a neural network to classify the existence of a fault. The second method uses parameters derived from the Koopman operator to create a prediction model with healthy data. This prediction model is then used to predict future system dynamics for a measured time evolution and compare that with direct measurements when future dynamics become available. Both methods are then tested via an experimental case study and the results are discussed.
{"title":"KOOPMAN OPERATOR BASED FAULT DIAGNOSTIC METHODS FOR MECHANICAL SYSTEMS","authors":"A. Nichifor, Yongzhi Qu","doi":"10.12783/shm2021/36299","DOIUrl":"https://doi.org/10.12783/shm2021/36299","url":null,"abstract":"Traditionally, dynamical systems can be simulated with physics-based model when the design parameters and material property are pre-known. However, when a system is deployed in field and has suffered potential degradation, a physics-based model might be infeasible to obtain. Moreover, the non-linearity and unknown coupling between the system and contacting constraints are often hard to determine accurately. The analysis of those systems becomes practically problematic. In this paper, the Koopman operator is used to learn and represent a dynamic system in a data driven manner. This paper proposes two methods of using the Koopman operator to extract and classify critical parameters of a non-linear dynamic mechanical system for fault diagnosis. The first method proposes a model to extract key features from a dynamic system and feed the features to a neural network to classify the existence of a fault. The second method uses parameters derived from the Koopman operator to create a prediction model with healthy data. This prediction model is then used to predict future system dynamics for a measured time evolution and compare that with direct measurements when future dynamics become available. Both methods are then tested via an experimental case study and the results are discussed.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"5 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":"114244472","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 article investigates multiple debonding in a glass fibre reinforced polymer structure (GFRPS) using nondestructive testing (NDT) based on ultrasonic guided waves (UGW) propagation. The piezoelectric transducers (PZT) attached to the material excite the UGW and the registered time signals are analyzed. The debonding are in various depths of the GFRPS. It was assessed using NDT-based tools. The presence of debonding and wave scattering based on the depth and location in GFRPS is studied. This is followed by using signal processing methods to visualize and analyze the different characteristics of the ultrasonic waves before and after the debonding. Thus an experimental-based approach to identify the debonding inside the GFRPS and its influence is studied.
{"title":"DEBONDING ANALYSIS OF COMPOSITE MATERIAL USING ULTRASONIC WAVE-BASED NDT METHODS","authors":"K. Balasubramaniam, T. Wandowski, P. Malinowski","doi":"10.12783/shm2021/36313","DOIUrl":"https://doi.org/10.12783/shm2021/36313","url":null,"abstract":"The article investigates multiple debonding in a glass fibre reinforced polymer structure (GFRPS) using nondestructive testing (NDT) based on ultrasonic guided waves (UGW) propagation. The piezoelectric transducers (PZT) attached to the material excite the UGW and the registered time signals are analyzed. The debonding are in various depths of the GFRPS. It was assessed using NDT-based tools. The presence of debonding and wave scattering based on the depth and location in GFRPS is studied. This is followed by using signal processing methods to visualize and analyze the different characteristics of the ultrasonic waves before and after the debonding. Thus an experimental-based approach to identify the debonding inside the GFRPS and its influence is studied.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"28 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":"114516298","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 study, we proposed a methodology for a technique to simultaneously estimate the mechanical parameters of vehicles and bridges and road surface roughness from vehicle vibration. The MCMC (Markov chain Monte Carlo) method was used to search for parameters from vehicle vibrations generated by numerical simulation. The results obtained are estimable even in the presence of bridge stiffness reduction, which suggests the possibility of bridge damage detection using vehicle vibration.
在本研究中，我们提出了一种从车辆振动中同时估计车辆和桥梁力学参数和路面粗糙度的技术方法。利用MCMC (Markov chain Monte Carlo)方法从数值模拟产生的车辆振动中搜索参数。即使在桥梁刚度降低的情况下，得到的结果也是可估计的，这表明利用车辆振动进行桥梁损伤检测是可能的。
{"title":"NUMERICAL STUDIES ON BRIDGE INSPECTION USING DATA OBTAINED FROM SENSORS ON VEHICLE","authors":"Kyosuke Yamamoto, Sachiyo Fujiwara, Kento Tsukada, Ryota Shin, Yukihiko Okada","doi":"10.12783/shm2021/36324","DOIUrl":"https://doi.org/10.12783/shm2021/36324","url":null,"abstract":"In this study, we proposed a methodology for a technique to simultaneously estimate the mechanical parameters of vehicles and bridges and road surface roughness from vehicle vibration. The MCMC (Markov chain Monte Carlo) method was used to search for parameters from vehicle vibrations generated by numerical simulation. The results obtained are estimable even in the presence of bridge stiffness reduction, which suggests the possibility of bridge damage detection using vehicle vibration.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"86 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":"125399183","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}
Railway tracks are used as mass transportation system for transporting large number of people and goods from place-to-place to keep the economy running smoothly. Hence it is inevitable to keep the tracks healthy for safe and on-time movement of trains. Traintracks are complex systems that contain ballast, sleepers, fasteners and rails. Therefore, monitoring only one/two elements (e.g., ballast/train-track) will not provide enough information to understand the overall performance of the railway tracks. To tackle such issue, herein a sensor fusion i.e., accelerometers, fiber-optic sensors, strategy is adopted and sensors are placed at different locations of a real rail-track. In order to measure the vibration signal four accelerometers are employed, first one is placed on the rail (between two sleepers), second one is installed on the rail but above the sleeper, third one is exactly on the sleeper, and last one is on the precast railway trough. In a first step, the investigation has focused into accelerometers data only. The tests are performed for the following loading conditions: (i) shaking the track via an APS400 type shaker, (ii) hitting the track by an impact hammer, and (iii) by passing a real train on the track. The time-series data are analyzed and the frequencies and spectrums are estimated via the use of fast Fourier transform (FFT). The changes of frequencies of the tested rail-track at different locations due to the various loading conditions are observed. In a later step, an autoregressive type time-series model has been developed and validated where the initially obtained results show good agreement with the measured data. The current findings will assist to monitor the rail-track for any further changes.
{"title":"MONITORING OF RAIL-TRACKS BASED ON MEASURED ACCELERATION DATA","authors":"M. S. Miah, W. Lienhart","doi":"10.12783/shm2021/36244","DOIUrl":"https://doi.org/10.12783/shm2021/36244","url":null,"abstract":"Railway tracks are used as mass transportation system for transporting large number of people and goods from place-to-place to keep the economy running smoothly. Hence it is inevitable to keep the tracks healthy for safe and on-time movement of trains. Traintracks are complex systems that contain ballast, sleepers, fasteners and rails. Therefore, monitoring only one/two elements (e.g., ballast/train-track) will not provide enough information to understand the overall performance of the railway tracks. To tackle such issue, herein a sensor fusion i.e., accelerometers, fiber-optic sensors, strategy is adopted and sensors are placed at different locations of a real rail-track. In order to measure the vibration signal four accelerometers are employed, first one is placed on the rail (between two sleepers), second one is installed on the rail but above the sleeper, third one is exactly on the sleeper, and last one is on the precast railway trough. In a first step, the investigation has focused into accelerometers data only. The tests are performed for the following loading conditions: (i) shaking the track via an APS400 type shaker, (ii) hitting the track by an impact hammer, and (iii) by passing a real train on the track. The time-series data are analyzed and the frequencies and spectrums are estimated via the use of fast Fourier transform (FFT). The changes of frequencies of the tested rail-track at different locations due to the various loading conditions are observed. In a later step, an autoregressive type time-series model has been developed and validated where the initially obtained results show good agreement with the measured data. The current findings will assist to monitor the rail-track for any further changes.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"59 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":"115309589","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 leaky Lamb wave has a broad application prospect in the fields of biosensing, concrete construction, composite material manufacturing, and so on. This paper first theoretically investigates the propagation characteristics of leaky Lamb waves when the rigid mold is loaded with water or viscous resin by one side in the semi-infinite space. As the thickness of the liquid increases, more and more energy leaks into the liquid through the solid-liquid interface. There is more energy leaking into the viscous resin than that in the water. The phase velocity and energy velocity decrease as the liquid increases. Then the amplitude reduction and phase delay of A0 mode due to the liquid loading from theoretical analysis were verified by subsequent experiments. One valid application of leaky Lamb waves in resin impregnation monitoring during the Vacuum Assisted Resin Infusion of composite materials manufacturing process was also investigated experimentally.
{"title":"THE PROPAGATION CHARACTERISTICS OF LEAKY LAMB WAVE AND APPLICATION FOR RESIN IMPREGNATION MONITORING","authors":"Xiao Liu, Yishou Wang, X. Qing","doi":"10.12783/shm2021/36312","DOIUrl":"https://doi.org/10.12783/shm2021/36312","url":null,"abstract":"The leaky Lamb wave has a broad application prospect in the fields of biosensing, concrete construction, composite material manufacturing, and so on. This paper first theoretically investigates the propagation characteristics of leaky Lamb waves when the rigid mold is loaded with water or viscous resin by one side in the semi-infinite space. As the thickness of the liquid increases, more and more energy leaks into the liquid through the solid-liquid interface. There is more energy leaking into the viscous resin than that in the water. The phase velocity and energy velocity decrease as the liquid increases. Then the amplitude reduction and phase delay of A0 mode due to the liquid loading from theoretical analysis were verified by subsequent experiments. One valid application of leaky Lamb waves in resin impregnation monitoring during the Vacuum Assisted Resin Infusion of composite materials manufacturing process was also investigated experimentally.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"8 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":"121277277","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, a new novel smart fatigue damage sensor (US Patent 8,746,077 B2) for continuous monitoring of fatigue health state of structural members of aircrafts is presented. The sensor has multiple parallel beams, each sensitive to different levels of fatigue lifetime. These beams are designed to fail prematurely but progressively as the sensor goes through the same fatigue cycles as the structural member it is attached to. Whenever fatigue level on an individual beam of the sensor exceeds the number of engineered fatigue cycles, that particular beam fails and sensor electronics can detect that failure and transmit this information wirelessly. Just like mileage signs on the road informing you about the distance left to your destination as you drive, multiple beams of the sensor serve similar purpose informing the user about the distance to failure progressively. Just as mileage signs can be placed at desired intervals, multiple beams can be engineered to give indication at desired fatigue milestones. This gives ability to monitor aging status of the structure and also help schedule predictive maintenance accordingly. The beams inside the sensor are designed to work based on different stress concentration factors (Notch Factors)/geometry to measure the level of structural fatigue health. The sensor needs to be mounted on the surface of structural member at fatigue critical locations just like strain gauges. Unlike strain gauges, a unique feature of the new sensor is its ability to operate without power source. This way it can serve for a long time without maintenance. Since sensor does not need power to operate, it can be embedded or mounted on critical components including composite structures or rotating helicopter shafts, gears, etc. After being attached to critical location of the real structure, the smart fatigue damage sensor goes through the same fatigue life experience of critical structural elements or mechanical components from the beginning of service life to the end. The fatigue sensing beams with different stress-strain and fatigue lifetime levels are designed to estimate the fatigue damage accumulation and remaining fatigue life of unidirectional and multidirectional structural or mechanical elements including composite structures. Since distributed fatigue sensor network system monitors the fatigue health conditions of structures periodically or on demand, the collected data can be used not only for condition-based fatigue life prediction but also for sensor based predictive fatigue maintenance and development. This new approach could also pave way to new fatigue design tools for fatigue sensitive complex, large and expensive engineering structures or mechanical systems of aircraft structures. Full paper will be concentrating design principles of the sensor based on Stress/Strain-Life Based Prediction principles.
{"title":"A NOVEL FATIGUE DAMAGE SENSOR FOR STRESS/STRAIN-LIFE BASED PREDICTION OF REMAINING FATIGUE LIFETIME OF LARGE AND COMPLEX STRUCTURES: AIRCRAFTS","authors":"Halit Kaplan, T. Ozkul","doi":"10.12783/shm2021/36274","DOIUrl":"https://doi.org/10.12783/shm2021/36274","url":null,"abstract":"In this paper, a new novel smart fatigue damage sensor (US Patent 8,746,077 B2) for continuous monitoring of fatigue health state of structural members of aircrafts is presented. The sensor has multiple parallel beams, each sensitive to different levels of fatigue lifetime. These beams are designed to fail prematurely but progressively as the sensor goes through the same fatigue cycles as the structural member it is attached to. Whenever fatigue level on an individual beam of the sensor exceeds the number of engineered fatigue cycles, that particular beam fails and sensor electronics can detect that failure and transmit this information wirelessly. Just like mileage signs on the road informing you about the distance left to your destination as you drive, multiple beams of the sensor serve similar purpose informing the user about the distance to failure progressively. Just as mileage signs can be placed at desired intervals, multiple beams can be engineered to give indication at desired fatigue milestones. This gives ability to monitor aging status of the structure and also help schedule predictive maintenance accordingly. The beams inside the sensor are designed to work based on different stress concentration factors (Notch Factors)/geometry to measure the level of structural fatigue health. The sensor needs to be mounted on the surface of structural member at fatigue critical locations just like strain gauges. Unlike strain gauges, a unique feature of the new sensor is its ability to operate without power source. This way it can serve for a long time without maintenance. Since sensor does not need power to operate, it can be embedded or mounted on critical components including composite structures or rotating helicopter shafts, gears, etc. After being attached to critical location of the real structure, the smart fatigue damage sensor goes through the same fatigue life experience of critical structural elements or mechanical components from the beginning of service life to the end. The fatigue sensing beams with different stress-strain and fatigue lifetime levels are designed to estimate the fatigue damage accumulation and remaining fatigue life of unidirectional and multidirectional structural or mechanical elements including composite structures. Since distributed fatigue sensor network system monitors the fatigue health conditions of structures periodically or on demand, the collected data can be used not only for condition-based fatigue life prediction but also for sensor based predictive fatigue maintenance and development. This new approach could also pave way to new fatigue design tools for fatigue sensitive complex, large and expensive engineering structures or mechanical systems of aircraft structures. Full paper will be concentrating design principles of the sensor based on Stress/Strain-Life Based Prediction principles.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"4 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":"115544938","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 development of aircraft structures requires many fatigue tests. These tests are usually carried out to validate the corresponding finite element and damage models and to prove the expected damage-tolerant behavior. Monitoring aircraft structures requires experienced staff and is very time-consuming and expensive as the recurring inspection of the structure is a tedious task. We propose a machine learning-based approach that exploits continuous load and strain measurement data to support structural health monitoring and to shift the inspection program towards predictive maintenance. The machine learning model is used for mapping loads onto local strains. With the trained model, different error measures between current measurements and the predicted values are determined. When a specific threshold value based on an error confidence level is exceeded, an alarm is set off, and appropriate actions can be taken. The approach is applied to several fatigue tests with two different types of structures and damage mechanisms. Various error measures and models are compared. The paper shows that, first, simple error measures, such as the root mean squared error, are sufficient and even outperform more sophisticated error distances for detecting cracks with continuous strain measurements. Second, the standard deviation of strain or rather the load-strain slope is a key feature to detect cracks. And third, machine learning models enable structural health monitoring with sensors that even have only small strain values.
{"title":"COMPARISON OF ERROR MEASURES AND MACHINE LEARNING METHODS FOR STRAIN-BASED STRUCTURAL HEALTH MONITORING","authors":"Simon Pfingstl, O. Tusch, M. Zimmermann","doi":"10.12783/shm2021/36289","DOIUrl":"https://doi.org/10.12783/shm2021/36289","url":null,"abstract":"The development of aircraft structures requires many fatigue tests. These tests are usually carried out to validate the corresponding finite element and damage models and to prove the expected damage-tolerant behavior. Monitoring aircraft structures requires experienced staff and is very time-consuming and expensive as the recurring inspection of the structure is a tedious task. We propose a machine learning-based approach that exploits continuous load and strain measurement data to support structural health monitoring and to shift the inspection program towards predictive maintenance. The machine learning model is used for mapping loads onto local strains. With the trained model, different error measures between current measurements and the predicted values are determined. When a specific threshold value based on an error confidence level is exceeded, an alarm is set off, and appropriate actions can be taken. The approach is applied to several fatigue tests with two different types of structures and damage mechanisms. Various error measures and models are compared. The paper shows that, first, simple error measures, such as the root mean squared error, are sufficient and even outperform more sophisticated error distances for detecting cracks with continuous strain measurements. Second, the standard deviation of strain or rather the load-strain slope is a key feature to detect cracks. And third, machine learning models enable structural health monitoring with sensors that even have only small strain values.","PeriodicalId":180083,"journal":{"name":"Proceedings of the 13th International Workshop on Structural Health Monitoring","volume":"95 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":"114885748","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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