An approach is proposed for the evaluation of the probability density functions (pdfs) of the modal parameters for an ensemble of nominally identical structures when there is only access to a single structure and the dispersion parameter is known. The approach combines the Eigensystem Realization Algorithm on sets of dynamic data, with an explicit non-parametric probabilistic method. A single structure, either a mathematical model or a prototype, are respectively used to obtain simulated data or measurements that are employed to build a discrete time state-space model description. The dispersion parameter is used to describe the uncertainty due to different sources such as the variability found in the population and the identification errors found in the noisy measurements from the experiments. With this approach, instead of propagating the uncertainties through the governing equations of the system, the distribution of the modal parameters of the whole ensemble is obtained by randomising the matrices in the state-space model with an efficient procedure. The applicability of the approach is shown through the analysis of a 2D0F mass-spring-damper system and a cantilever system. These results show that if the source of uncertainty is unknown and it is possible to specify an overall level of uncertainty, by having access to a single system measurements' it is possible to evaluate the resulting pdfs on the modal parameters. It was also found that high values of the dispersion parameter may lead to non-physical results such as negative damping ratios values.
{"title":"On the Combination of Random Matrix Theory with Measurements On a Single Structure","authors":"F. Igea, M. Chatzis, A. Cicirello","doi":"10.1115/1.4054172","DOIUrl":"https://doi.org/10.1115/1.4054172","url":null,"abstract":"\u0000 An approach is proposed for the evaluation of the probability density functions (pdfs) of the modal parameters for an ensemble of nominally identical structures when there is only access to a single structure and the dispersion parameter is known. The approach combines the Eigensystem Realization Algorithm on sets of dynamic data, with an explicit non-parametric probabilistic method. A single structure, either a mathematical model or a prototype, are respectively used to obtain simulated data or measurements that are employed to build a discrete time state-space model description. The dispersion parameter is used to describe the uncertainty due to different sources such as the variability found in the population and the identification errors found in the noisy measurements from the experiments. With this approach, instead of propagating the uncertainties through the governing equations of the system, the distribution of the modal parameters of the whole ensemble is obtained by randomising the matrices in the state-space model with an efficient procedure. The applicability of the approach is shown through the analysis of a 2D0F mass-spring-damper system and a cantilever system. These results show that if the source of uncertainty is unknown and it is possible to specify an overall level of uncertainty, by having access to a single system measurements' it is possible to evaluate the resulting pdfs on the modal parameters. It was also found that high values of the dispersion parameter may lead to non-physical results such as negative damping ratios values.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90303387","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}
Reliability can be predicted by a limit-state function, which may vary with time and space. This work extends the envelope method for a time-dependent limit-state function to a time- and space-dependent limit-state function. The proposed method uses the envelope function of time- and space-dependent limit-state function. It at first searches for the most probable point (MPP) of the envelope function using the sequential efficient global optimization in the domain of the space and time under consideration. Then the envelope function is approximated by a quadratic function at the MPP, for which analytic gradient and Hessian matrix of the envelope function are derived. Subsequently, the second-order saddlepoint approximation method is employed to estimate the probability of failure. Three examples demonstrate the effectiveness of the proposed method. The method can efficiently produce an accurate reliability prediction when the MPP is within the domain of the space and time under consideration.
{"title":"Envelope Method for Time- and Space-Dependent Reliability Prediction","authors":"Wu Hao, Xiaoping Du","doi":"10.1115/1.4054171","DOIUrl":"https://doi.org/10.1115/1.4054171","url":null,"abstract":"\u0000 Reliability can be predicted by a limit-state function, which may vary with time and space. This work extends the envelope method for a time-dependent limit-state function to a time- and space-dependent limit-state function. The proposed method uses the envelope function of time- and space-dependent limit-state function. It at first searches for the most probable point (MPP) of the envelope function using the sequential efficient global optimization in the domain of the space and time under consideration. Then the envelope function is approximated by a quadratic function at the MPP, for which analytic gradient and Hessian matrix of the envelope function are derived. Subsequently, the second-order saddlepoint approximation method is employed to estimate the probability of failure. Three examples demonstrate the effectiveness of the proposed method. The method can efficiently produce an accurate reliability prediction when the MPP is within the domain of the space and time under consideration.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88228667","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}
More A. Vishwendra, Pratiksha S. Salunkhe, Shivanjali V. Patil, Sumit A. Shinde, P. V. Shinde, R. Desavale, P. M. Jadhav, Dr. Nagaraj V. Dharwadkar
A novel method is proposed in this work for the classification of fault in the ball bearings. Applications of K-nearest neighbor (KNN) techniques are increasing, which redefines the state-of-the-art technology for defect diagnosis and classification. Vibration characteristics of deep groove ball bearing with different defects are studied in this paper. Experimentation is conducted at different loads and speeds with artificially created defects, and vibration data are processed using kurtosis to find frequency band of interest and amplitude demodulation (Envelope spectrum analysis). Bearing fault amplitudes are extracted from the filtered signal spectrum at bearing characteristic frequency. The decision of fault classification is made using a KNN machine learning classifier by training feature data. The training features are created using characteristics amplitude at different fault and bearing conditions. The results showed that the KNN's accuracies are 100% and 97.3% when applied to two different experimental databases. The quantitative results of the KNN classifier are applied as the guidance for investigating the type of defects of bearing. The KNN Classifier method proved to be an effective method to quantify defects and significantly improve classification efficiency.
{"title":"A Novel Method to Classify Rolling Element Bearing Faults Using K-Nearest Neighbor Machine Learning Algorithm","authors":"More A. Vishwendra, Pratiksha S. Salunkhe, Shivanjali V. Patil, Sumit A. Shinde, P. V. Shinde, R. Desavale, P. M. Jadhav, Dr. Nagaraj V. Dharwadkar","doi":"10.1115/1.4053760","DOIUrl":"https://doi.org/10.1115/1.4053760","url":null,"abstract":"\u0000 A novel method is proposed in this work for the classification of fault in the ball bearings. Applications of K-nearest neighbor (KNN) techniques are increasing, which redefines the state-of-the-art technology for defect diagnosis and classification. Vibration characteristics of deep groove ball bearing with different defects are studied in this paper. Experimentation is conducted at different loads and speeds with artificially created defects, and vibration data are processed using kurtosis to find frequency band of interest and amplitude demodulation (Envelope spectrum analysis). Bearing fault amplitudes are extracted from the filtered signal spectrum at bearing characteristic frequency. The decision of fault classification is made using a KNN machine learning classifier by training feature data. The training features are created using characteristics amplitude at different fault and bearing conditions. The results showed that the KNN's accuracies are 100% and 97.3% when applied to two different experimental databases. The quantitative results of the KNN classifier are applied as the guidance for investigating the type of defects of bearing. The KNN Classifier method proved to be an effective method to quantify defects and significantly improve classification efficiency.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87610944","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}
Dynamic window approach (DWA) is one of the most widely used algorithms for local path planning and autonomous navigation. Although many successful examples have been shown under various operation conditions, to the authors' best knowledge, there is a lack of systematic reliability analysis, its further design improvement, and systems operation guidelines for meeting reliability requirement under different operation conditions. Several goals can be defined for a successful path planning and autonomous navigation. Among them, assurance of the collision avoidance and reaching the goal with less time are pivotal requirements, yet such reliability analysis is rarely conducted in a rigorous manner. Furthermore, design improvement and systems operation design for meeting the reliability constraints do not exist in literature in this area. This paper addresses such a research gap for autonomous navigation reliability analysis and further conducts design improvement and characterizes systems operation conditions for meeting the collision avoidance reliability using the DWA. To address the technical challenges associated with limited number of simulations or experiments, reliability analysis is conducted using Bayesian statistics combined with the Monte Carlo simulation (MCS). Design improvement and reliable operation conditions can then be conducted based on the reliability analysis. Results indicate that performance reliability of the DWA is sensitive to its parameter configuration, which can be improved through reliability-based design optimization. With characterized collision avoidance reliability constraints, performance reliability of the DWA can be ensured through adjusting its operation parameters.
{"title":"Systematical Collision Avoidance Reliability Analysis and Characterization of Reliable System Operation for Autonomous Navigation Using the Dynamic Window Approach","authors":"E. Torkamani, Zhimin Xi","doi":"10.1115/1.4053941","DOIUrl":"https://doi.org/10.1115/1.4053941","url":null,"abstract":"\u0000 Dynamic window approach (DWA) is one of the most widely used algorithms for local path planning and autonomous navigation. Although many successful examples have been shown under various operation conditions, to the authors' best knowledge, there is a lack of systematic reliability analysis, its further design improvement, and systems operation guidelines for meeting reliability requirement under different operation conditions. Several goals can be defined for a successful path planning and autonomous navigation. Among them, assurance of the collision avoidance and reaching the goal with less time are pivotal requirements, yet such reliability analysis is rarely conducted in a rigorous manner. Furthermore, design improvement and systems operation design for meeting the reliability constraints do not exist in literature in this area. This paper addresses such a research gap for autonomous navigation reliability analysis and further conducts design improvement and characterizes systems operation conditions for meeting the collision avoidance reliability using the DWA. To address the technical challenges associated with limited number of simulations or experiments, reliability analysis is conducted using Bayesian statistics combined with the Monte Carlo simulation (MCS). Design improvement and reliable operation conditions can then be conducted based on the reliability analysis. Results indicate that performance reliability of the DWA is sensitive to its parameter configuration, which can be improved through reliability-based design optimization. With characterized collision avoidance reliability constraints, performance reliability of the DWA can be ensured through adjusting its operation parameters.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78874091","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 new method for reliable fatigue life prediction in metal structural components is developed which quantifies uncertainties using interval variables. Using this crack-initiation-based method, first, the uncertainties in laboratory test data for the fatigue failure of a structural detail are enumerated. This uncertainty quantification is performed through an interval-based enveloping procedure that relates the interval stress ranges to the number of cycles to failure. This will lead to the construction of an interval S-N relationship. Next, the uncertainties in field test data are enumerated in the extremum values of each stress range, as intervals, leading to the construction of interval stress ranges. For both the laboratory and field data uncertainty analyses, the mean stress effects are considered. Next, the interval damage accumulated over the duration of the field data is determined using the constructed interval S-N relationship and the obtained interval stress ranges. Then, the interval existing damage and interval remaining life are determined. Finally, as a conservative measure, the minimum remaining fatigue life is obtained in which, all uncertainties are considered. A numerical example illustrating the developed method is presented, and the results are compared with results obtained by both Monte Carlo simulation and optimization. Using this method, for the numerical example considered, it is shown that the results for bounds on the existing damage and the remaining fatigue life are sharp. Moreover, due to its set-based approach, the method is significantly more computationally efficient when compared with iterative procedures.
{"title":"Fatigue Life Prediction of Structures with Interval Uncertainty","authors":"Michael Desch, M. Modares","doi":"10.1115/1.4053940","DOIUrl":"https://doi.org/10.1115/1.4053940","url":null,"abstract":"\u0000 A new method for reliable fatigue life prediction in metal structural components is developed which quantifies uncertainties using interval variables. Using this crack-initiation-based method, first, the uncertainties in laboratory test data for the fatigue failure of a structural detail are enumerated. This uncertainty quantification is performed through an interval-based enveloping procedure that relates the interval stress ranges to the number of cycles to failure. This will lead to the construction of an interval S-N relationship. Next, the uncertainties in field test data are enumerated in the extremum values of each stress range, as intervals, leading to the construction of interval stress ranges. For both the laboratory and field data uncertainty analyses, the mean stress effects are considered. Next, the interval damage accumulated over the duration of the field data is determined using the constructed interval S-N relationship and the obtained interval stress ranges. Then, the interval existing damage and interval remaining life are determined. Finally, as a conservative measure, the minimum remaining fatigue life is obtained in which, all uncertainties are considered. A numerical example illustrating the developed method is presented, and the results are compared with results obtained by both Monte Carlo simulation and optimization. Using this method, for the numerical example considered, it is shown that the results for bounds on the existing damage and the remaining fatigue life are sharp. Moreover, due to its set-based approach, the method is significantly more computationally efficient when compared with iterative procedures.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87457545","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}
Uncertainties associated with the prediction of the Remaining Useful Life (RUL) of random degradation equipment are influenced by such factors as time-varying uncertainty, individual difference, and measurement error. Given this, a predictive method for the RUL of an aero -engine with three layers of uncertainty was proposed. Firstly, historical condition monitoring data was used to generate a Composite Health Index (CHI) for characterizing the performance degradation of the engine. Then a nonlinear Wiener degradation model is built considering three layers of uncertainty. Secondly, the maximum likelihood method is applied to obtain the estimates of the priori distribution of the random coefficients in the degradation model. Then, the degradation states were updated synchronously by applying the Kalman Filtering (KF) algorithm and constructing the state-space model. Finally, the Probability Density Function (PDF) of the RUL with three layers of uncertainty was deduced from the total probability formula. A numerical example and a case study comparing several representative methods in the literature were presented using the aero-engine data. The simulation example analysis shows that the proposed method can significantly improve RUL prediction accuracy, and thus it has a particular engineering application value.
{"title":"Remaining Useful Life Prediction Method of Aero Engine With Multilayer Uncertainty","authors":"Ma JiaShun, JianFeng Wu, Yong Zhang","doi":"10.1115/1.4053906","DOIUrl":"https://doi.org/10.1115/1.4053906","url":null,"abstract":"\u0000 Uncertainties associated with the prediction of the Remaining Useful Life (RUL) of random degradation equipment are influenced by such factors as time-varying uncertainty, individual difference, and measurement error. Given this, a predictive method for the RUL of an aero -engine with three layers of uncertainty was proposed. Firstly, historical condition monitoring data was used to generate a Composite Health Index (CHI) for characterizing the performance degradation of the engine. Then a nonlinear Wiener degradation model is built considering three layers of uncertainty. Secondly, the maximum likelihood method is applied to obtain the estimates of the priori distribution of the random coefficients in the degradation model. Then, the degradation states were updated synchronously by applying the Kalman Filtering (KF) algorithm and constructing the state-space model. Finally, the Probability Density Function (PDF) of the RUL with three layers of uncertainty was deduced from the total probability formula. A numerical example and a case study comparing several representative methods in the literature were presented using the aero-engine data. The simulation example analysis shows that the proposed method can significantly improve RUL prediction accuracy, and thus it has a particular engineering application value.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78083195","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}
Important facilities constructed during last decades of 20th century are near completion of design life. For extending their service life or to evaluate these for new demands (loads), assessment of strength of concrete in existing structure becomes necessary, a task generally performed with non-destructive tests (NDT); ultrasonic pulse velocity and rebound hammer being most commonly executed. Compressive strength is estimated using empirical expressions relating NDT to partially destructive tests (PDT) such as core test. For development of structure-specific expressions, results of adequate number (depending on variability and desired confidence level) of PDT are essential but these might not be available due to operational constraints. Correlation expressions from literature could be used in such cases but having been developed for different ingredients, curing regimes, and environmental exposure conditions, there would be associated uncertainties. A practical method for estimation of these uncertainties is not readily available in literature. This article proposes the statistical approach of re-sampling for quantifying uncertainty of indirect strength estimates using expressions from literature. Parametric (probability distribution) and nonparametric (bootstrap) tools are employed and demonstrated with a case study from India. Both parametric and nonparametric approaches could capture across-member variability whereas overall uncertainty incorporation as well as repeatability was better in nonparametric approach. Parametric approach is traditionally used and well accepted by practitioners in contrast to nonparametric methods, which have certain advantages. The detailed methodology enumerated in the article would be very useful for practitioners across the world.
{"title":"Estimating Concrete Strength From Non-Destructive Testing with Few Core Tests Considering Uncertainties","authors":"S. Dauji, Soubhagya Karmakar","doi":"10.1115/1.4053639","DOIUrl":"https://doi.org/10.1115/1.4053639","url":null,"abstract":"\u0000 Important facilities constructed during last decades of 20th century are near completion of design life. For extending their service life or to evaluate these for new demands (loads), assessment of strength of concrete in existing structure becomes necessary, a task generally performed with non-destructive tests (NDT); ultrasonic pulse velocity and rebound hammer being most commonly executed. Compressive strength is estimated using empirical expressions relating NDT to partially destructive tests (PDT) such as core test. For development of structure-specific expressions, results of adequate number (depending on variability and desired confidence level) of PDT are essential but these might not be available due to operational constraints. Correlation expressions from literature could be used in such cases but having been developed for different ingredients, curing regimes, and environmental exposure conditions, there would be associated uncertainties. A practical method for estimation of these uncertainties is not readily available in literature. This article proposes the statistical approach of re-sampling for quantifying uncertainty of indirect strength estimates using expressions from literature. Parametric (probability distribution) and nonparametric (bootstrap) tools are employed and demonstrated with a case study from India. Both parametric and nonparametric approaches could capture across-member variability whereas overall uncertainty incorporation as well as repeatability was better in nonparametric approach. Parametric approach is traditionally used and well accepted by practitioners in contrast to nonparametric methods, which have certain advantages. The detailed methodology enumerated in the article would be very useful for practitioners across the world.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76157259","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}
D. Cevasco, J. Tautz-Weinert, M. Richmond, A. Sobey, A. Kolios
Structural failures of offshore wind turbine substructures might be less likely than failures of other equipment of the wind turbine generator but pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations like inspections and maintenance, thus remote monitoring shows promise for cost-efficient structural integrity management. This work is aimed to investigate the feasibility of the two-level detection, in terms of anomaly identification and localisation, in the jacket structure of an offshore wind turbine. A monitoring scheme is developed based on a database of modal properties of the structure for different scenarios. The method identifies the correct anomaly scenario based on three types of modal indicators, namely natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximise the separability of anomaly scenarios. A Fuzzy clustering algorithm is trained to predict the membership of new data to the scenarios in the database. In a case study, extreme scour phenomena and jacket member integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and localise the simulated scenarios via the global monitoring of an offshore wind jacket structure.
{"title":"A Damage Detection and Location Scheme for Offshore Wind Turbine Jacket Structures Based On Global Modal Properties","authors":"D. Cevasco, J. Tautz-Weinert, M. Richmond, A. Sobey, A. Kolios","doi":"10.1115/1.4053659","DOIUrl":"https://doi.org/10.1115/1.4053659","url":null,"abstract":"\u0000 Structural failures of offshore wind turbine substructures might be less likely than failures of other equipment of the wind turbine generator but pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations like inspections and maintenance, thus remote monitoring shows promise for cost-efficient structural integrity management. This work is aimed to investigate the feasibility of the two-level detection, in terms of anomaly identification and localisation, in the jacket structure of an offshore wind turbine. A monitoring scheme is developed based on a database of modal properties of the structure for different scenarios. The method identifies the correct anomaly scenario based on three types of modal indicators, namely natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximise the separability of anomaly scenarios. A Fuzzy clustering algorithm is trained to predict the membership of new data to the scenarios in the database. In a case study, extreme scour phenomena and jacket member integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and localise the simulated scenarios via the global monitoring of an offshore wind jacket structure.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90262223","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. Nellippallil, P. Berthelson, L. Peterson, R. Prabhu
Government agencies, globally, strive to minimize the likelihood and frequency of human death and severe injury on road transport systems. From an engineering design standpoint, the minimization of these road accident effects on occupants becomes a critical design goal. This necessitates the quantification and management of injury risks on the human body in response to several vehicular impact variables and their associated uncertainties for different crash scenarios. In this paper, we present a decision-based, robust design framework to quantify and manage the impact-based injury risks on occupants for different computational model-based car crash scenarios. The key functionality offered is the designer's capability to conduct robust concept exploration focused on managing the selected impact variables and associated uncertainties, such that injury risks are controlled within acceptable levels. The framework's efficacy is tested for near-side impact scenarios with impact velocity and angle of impact as the critical variables of interest. Two injury criteria, namely, Head Injury Criterion (HIC) and Lateral Neck Injury Criteria (Lateral Nij), are selected to quantitatively measure the head and neck injury risks in each crash simulation. Using the framework, a robust design problem is formulated to determine the combination of impact variables that best satisfice the injury goals defined. The framework and associated design constructs are generic and support the formulation and decision-based robust concept exploration of similar problems involving models under uncertainty. Our focus in this paper is on the framework rather than the results per se.
{"title":"A Computational Framework for Human-centric Vehicular Crashworthiness Design and Decision-Making Under Uncertainty","authors":"A. Nellippallil, P. Berthelson, L. Peterson, R. Prabhu","doi":"10.1115/1.4053515","DOIUrl":"https://doi.org/10.1115/1.4053515","url":null,"abstract":"\u0000 Government agencies, globally, strive to minimize the likelihood and frequency of human death and severe injury on road transport systems. From an engineering design standpoint, the minimization of these road accident effects on occupants becomes a critical design goal. This necessitates the quantification and management of injury risks on the human body in response to several vehicular impact variables and their associated uncertainties for different crash scenarios. In this paper, we present a decision-based, robust design framework to quantify and manage the impact-based injury risks on occupants for different computational model-based car crash scenarios. The key functionality offered is the designer's capability to conduct robust concept exploration focused on managing the selected impact variables and associated uncertainties, such that injury risks are controlled within acceptable levels. The framework's efficacy is tested for near-side impact scenarios with impact velocity and angle of impact as the critical variables of interest. Two injury criteria, namely, Head Injury Criterion (HIC) and Lateral Neck Injury Criteria (Lateral Nij), are selected to quantitatively measure the head and neck injury risks in each crash simulation. Using the framework, a robust design problem is formulated to determine the combination of impact variables that best satisfice the injury goals defined. The framework and associated design constructs are generic and support the formulation and decision-based robust concept exploration of similar problems involving models under uncertainty. Our focus in this paper is on the framework rather than the results per se.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79357624","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}
Data-driven wind turbine performance predictions, such as power and loads, are important for planning and operation. Current methods do not take site-specific conditions such as turbulence intensity and shear into account, which could result in errors of up to 10%. In this work, four different machine learning models (k-nearest neighbors regression, random forest regression, extreme gradient boosting regression and artificial neural networks (ANN) are trained and tested, firstly on a simulation dataset and then on a real dataset. It is found that machine learning methods that take site-specific conditions into account can improve prediction accuracy by a factor of two to three, depening on the error indicator chosen. Similar results are observed for multi-output ANNs for simulated in- and out-of-plane rotor blade tip deflection and root loads. Future work focuses on understanding transferability of results between different turbines within a wind farm and between different wind turbine types.
{"title":"Improving Site-Dependent Wind Turbine Performance Prediction Accuracy Using Machine Learning","authors":"S. Barber, F. Hammer, A. Tica","doi":"10.1115/1.4053513","DOIUrl":"https://doi.org/10.1115/1.4053513","url":null,"abstract":"\u0000 Data-driven wind turbine performance predictions, such as power and loads, are important for planning and operation. Current methods do not take site-specific conditions such as turbulence intensity and shear into account, which could result in errors of up to 10%. In this work, four different machine learning models (k-nearest neighbors regression, random forest regression, extreme gradient boosting regression and artificial neural networks (ANN) are trained and tested, firstly on a simulation dataset and then on a real dataset. It is found that machine learning methods that take site-specific conditions into account can improve prediction accuracy by a factor of two to three, depening on the error indicator chosen. Similar results are observed for multi-output ANNs for simulated in- and out-of-plane rotor blade tip deflection and root loads. Future work focuses on understanding transferability of results between different turbines within a wind farm and between different wind turbine types.","PeriodicalId":44694,"journal":{"name":"ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B-Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87593868","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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