Ammar Chakhrit, Imene Djelamda, Mohammed Bougofa, Islam H. M. Guetarni, Abderraouf Bouafia, Mohammed Chennoufi
Failure mode effects and criticality analysis (FMECA) is widely employed across industries to recognize and reduce possible failures. Despite its extensive usage, FMECA encounters challenges in decision‐making. In this paper, a new fuzzy resilience‐based RPN model is created to develop the FMECA method. The fuzzy model transcends the limitations associated with traditional risk priority number calculations by incorporating factors beyond frequency, severity, and detection. This extension includes considerations impacting system cost, sustainability, and safety, providing a more comprehensive risk assessment. In addition, to create trust in decision‐makers, a robust assessment approach is suggested, integrating three methodologies. In the initial phase, the fuzzy analytical hierarchy process and the grey relation analysis method are used to determine the subjective weights of different risk factors and resolve the flaws associated with the deficiency of constructed fuzzy inference rules. In the second phase, an entropy method is applied to handle the uncertainty of individual weightage calculated and capture different conflicting experts' views. The suggested approach is validated through a case study involving a gas turbine. The results demonstrate significant differences in failure mode prioritization between different approaches. The introduction of MTTR addresses critical shortcomings in traditional FMECA, enhancing predictive capabilities. Furthermore, the hybrid approach improved criticality assessment and failure mode ranking, classifying failure modes into fifteen categories, aiding decision‐making, and applying appropriate risk mitigation measures. Overall, the findings validate the efficacy of the proposed approach in addressing uncertainties and divergent expert judgments for risk assessment in complex systems.
{"title":"Integrating fuzzy logic and multi‐criteria decision‐making in a hybrid FMECA for robust risk prioritization","authors":"Ammar Chakhrit, Imene Djelamda, Mohammed Bougofa, Islam H. M. Guetarni, Abderraouf Bouafia, Mohammed Chennoufi","doi":"10.1002/qre.3601","DOIUrl":"https://doi.org/10.1002/qre.3601","url":null,"abstract":"Failure mode effects and criticality analysis (FMECA) is widely employed across industries to recognize and reduce possible failures. Despite its extensive usage, FMECA encounters challenges in decision‐making. In this paper, a new fuzzy resilience‐based RPN model is created to develop the FMECA method. The fuzzy model transcends the limitations associated with traditional risk priority number calculations by incorporating factors beyond frequency, severity, and detection. This extension includes considerations impacting system cost, sustainability, and safety, providing a more comprehensive risk assessment. In addition, to create trust in decision‐makers, a robust assessment approach is suggested, integrating three methodologies. In the initial phase, the fuzzy analytical hierarchy process and the grey relation analysis method are used to determine the subjective weights of different risk factors and resolve the flaws associated with the deficiency of constructed fuzzy inference rules. In the second phase, an entropy method is applied to handle the uncertainty of individual weightage calculated and capture different conflicting experts' views. The suggested approach is validated through a case study involving a gas turbine. The results demonstrate significant differences in failure mode prioritization between different approaches. The introduction of MTTR addresses critical shortcomings in traditional FMECA, enhancing predictive capabilities. Furthermore, the hybrid approach improved criticality assessment and failure mode ranking, classifying failure modes into fifteen categories, aiding decision‐making, and applying appropriate risk mitigation measures. Overall, the findings validate the efficacy of the proposed approach in addressing uncertainties and divergent expert judgments for risk assessment in complex systems.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of the high‐end equipment technology, the performance requirements of the internal gear pump (IGP) under high pressure are also increasing. However, the increase of working pressure will lead to the instability of gear pump performance in terms of volumetric efficiency, noise, reliability and so on, it is necessary to reasonably evaluate the reliability level of high‐pressure IGP. The reliability analysis of the high‐pressure IGP is carried out from the aspects of flow, noise, and gear strength in this paper. First, the output flow rate and far‐field flow‐induced noise of the high‐pressure IGP were obtained through fluid numerical simulation, and experimental verification was conducted. Then, based on the time‐varying meshing stiffness, backlash function and static transmission error of the gear pair, a nonlinear dynamic model of the internal meshing gear pair was established. The time‐varying meshing force was obtained through the dynamic model of the gear pair, and then the tooth contact stress and tooth root bending stress were obtained. Finally, considering the uncertain factors affecting the performance of the high‐pressure IGP, Latin hypercube sampling (LHS) combined with dendrite network (DD) was used for random response modeling. The performance reliability of the high‐pressure IGP, including output flow rate, far‐field flow‐induced noise, and the strength of gear pair, were estimated based on the fourth moment‐based saddlepoint approximation (FMSA). The reliability analysis results can provide a theoretical basis for the structural optimization design of the high‐pressure IGP.
{"title":"Performance reliability evaluation of high‐pressure internal gear pump","authors":"Yu Tang, Hao Lu, Zhencai Zhu, Zhiyuan Shi, Beilian Xu","doi":"10.1002/qre.3585","DOIUrl":"https://doi.org/10.1002/qre.3585","url":null,"abstract":"With the development of the high‐end equipment technology, the performance requirements of the internal gear pump (IGP) under high pressure are also increasing. However, the increase of working pressure will lead to the instability of gear pump performance in terms of volumetric efficiency, noise, reliability and so on, it is necessary to reasonably evaluate the reliability level of high‐pressure IGP. The reliability analysis of the high‐pressure IGP is carried out from the aspects of flow, noise, and gear strength in this paper. First, the output flow rate and far‐field flow‐induced noise of the high‐pressure IGP were obtained through fluid numerical simulation, and experimental verification was conducted. Then, based on the time‐varying meshing stiffness, backlash function and static transmission error of the gear pair, a nonlinear dynamic model of the internal meshing gear pair was established. The time‐varying meshing force was obtained through the dynamic model of the gear pair, and then the tooth contact stress and tooth root bending stress were obtained. Finally, considering the uncertain factors affecting the performance of the high‐pressure IGP, Latin hypercube sampling (LHS) combined with dendrite network (DD) was used for random response modeling. The performance reliability of the high‐pressure IGP, including output flow rate, far‐field flow‐induced noise, and the strength of gear pair, were estimated based on the fourth moment‐based saddlepoint approximation (FMSA). The reliability analysis results can provide a theoretical basis for the structural optimization design of the high‐pressure IGP.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yaqiong Lv, Xiaohu Zhang, Yiwei Cheng, Carman K. M. Lee
With the advent of intelligent manufacturing era, higher requirements are put forward for the fault diagnosis technology of machinery. The existing data‐driven approaches either rely on specialized empirical knowledge for feature analysis, or adopt single deep neural network topology structure for automatic feature extraction with compromise of certain information loss especially the time‐series information's sacrifice, which both eventually affect the diagnosis accuracy. To address the issue, this paper proposes a novel multi‐temporal correlation feature fusion net (MTCFF‐Net) for intelligent fault diagnosis, which can capture and retain time‐series fault feature information from different dimensions. MTCFF‐Net contains four sub‐networks, which are long and short‐term memory (LSTM) sub‐network, Gramian angular summation field (GASF)‐GhostNet sub‐network and Markov transition field (MTF)‐GhostNet sub‐network and feature fusion sub‐network. Features of different dimensional are extracted through parallel LSTM sub‐network, GASF‐GhostNet sub‐network and MTF‐GhostNet sub‐network, and then fused by feature fusion sub‐network for accurate fault diagnosis. Two fault diagnosis experimental studies on bearings are implemented to validate the effectiveness and generalization of the proposed MTCFF‐Net. Experimental results demonstrate that the proposed model is superior to other comparative approaches.
{"title":"Intelligent fault diagnosis of machinery based on hybrid deep learning with multi temporal correlation feature fusion","authors":"Yaqiong Lv, Xiaohu Zhang, Yiwei Cheng, Carman K. M. Lee","doi":"10.1002/qre.3597","DOIUrl":"https://doi.org/10.1002/qre.3597","url":null,"abstract":"With the advent of intelligent manufacturing era, higher requirements are put forward for the fault diagnosis technology of machinery. The existing data‐driven approaches either rely on specialized empirical knowledge for feature analysis, or adopt single deep neural network topology structure for automatic feature extraction with compromise of certain information loss especially the time‐series information's sacrifice, which both eventually affect the diagnosis accuracy. To address the issue, this paper proposes a novel multi‐temporal correlation feature fusion net (MTCFF‐Net) for intelligent fault diagnosis, which can capture and retain time‐series fault feature information from different dimensions. MTCFF‐Net contains four sub‐networks, which are long and short‐term memory (LSTM) sub‐network, Gramian angular summation field (GASF)‐GhostNet sub‐network and Markov transition field (MTF)‐GhostNet sub‐network and feature fusion sub‐network. Features of different dimensional are extracted through parallel LSTM sub‐network, GASF‐GhostNet sub‐network and MTF‐GhostNet sub‐network, and then fused by feature fusion sub‐network for accurate fault diagnosis. Two fault diagnosis experimental studies on bearings are implemented to validate the effectiveness and generalization of the proposed MTCFF‐Net. Experimental results demonstrate that the proposed model is superior to other comparative approaches.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Advances and novel applications in systems reliability and safety engineering (selected papers of the International Conference of SRSE 2022)","authors":"Weiwen Peng, Ancha Xu, Jiawen Hu","doi":"10.1002/qre.3580","DOIUrl":"https://doi.org/10.1002/qre.3580","url":null,"abstract":"","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141173043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cheng Qian, Wenjuan Li, Shengxing Wei, Bo Sun, Yi Ren
When using Monte Carlo simulation involving repeated finite element analysis (FEA) to perform fatigue reliability evaluation for an impeller, a variety of uncertainties should be considered to ensure the comprehensiveness of fatigue predictions. These uncertainties include the aleatory uncertainty from the geometric, material and load condition, and epistemic uncertainty from the parameters of the physics‐of‐failure (PoF) model to yield fatigue prediction. However, the latter uncertainty is often ignored in fatigue reliability analysis. And the reliability assessment will become computationally unaffordable and inefficient when there are many random variables involved, as an enormous amount of FEAs are demanded. To address this problem, a Whale Optimization Algorithm‐extreme gradient boosting (WOA‐XGBoost) surrogate model is developed, based on relatively few FEA results obtained using a Latin hypercube sampling (LHS). Its strengths lie in the interpretability of the design variables and effective determination of fine‐tuned hyperparameters. A case study on an impeller is conducted considering uncertainties from 11 input variables, where an efficient XGBoost model with an R2 greater than 0.93 on test set is established using 400 samples from practical FEAs. In addition, the importance analysis indicates that elasticity modulus and density play the greatest impact on the maximum strain, showing a combined importance of 82.3%. Furthermore, the reliability assessment results under fatigue parameter derived from the Median method tend to be more conservative compared to those obtained from the Seeger method.
{"title":"Fatigue reliability evaluation for impellers with consideration of multi‐source uncertainties using a WOA‐XGBoost surrogate model","authors":"Cheng Qian, Wenjuan Li, Shengxing Wei, Bo Sun, Yi Ren","doi":"10.1002/qre.3584","DOIUrl":"https://doi.org/10.1002/qre.3584","url":null,"abstract":"When using Monte Carlo simulation involving repeated finite element analysis (FEA) to perform fatigue reliability evaluation for an impeller, a variety of uncertainties should be considered to ensure the comprehensiveness of fatigue predictions. These uncertainties include the aleatory uncertainty from the geometric, material and load condition, and epistemic uncertainty from the parameters of the physics‐of‐failure (PoF) model to yield fatigue prediction. However, the latter uncertainty is often ignored in fatigue reliability analysis. And the reliability assessment will become computationally unaffordable and inefficient when there are many random variables involved, as an enormous amount of FEAs are demanded. To address this problem, a Whale Optimization Algorithm‐extreme gradient boosting (WOA‐XGBoost) surrogate model is developed, based on relatively few FEA results obtained using a Latin hypercube sampling (LHS). Its strengths lie in the interpretability of the design variables and effective determination of fine‐tuned hyperparameters. A case study on an impeller is conducted considering uncertainties from 11 input variables, where an efficient XGBoost model with an <jats:italic>R</jats:italic><jats:sup>2</jats:sup> greater than 0.93 on test set is established using 400 samples from practical FEAs. In addition, the importance analysis indicates that elasticity modulus and density play the greatest impact on the maximum strain, showing a combined importance of 82.3%. Furthermore, the reliability assessment results under fatigue parameter derived from the Median method tend to be more conservative compared to those obtained from the Seeger method.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Foldover designs often have attractive properties. Among these is that the effects can be divided into two orthogonal subspaces, one for odd effects and one for even effects. In this paper, we introduce a new method for analyzing foldover designs called the decoupling method that exploits this trait. Utilizing mirror image pair runs, two new responses are created, where each of them is only affected by effects in one of the orthogonal subspaces. Thereby the analysis of odd and even effects can be performed in two independent steps, enabling use of standard statistical procedures and formal testing of the presence of higher‐order interactions. The method is demonstrated on real data from a foldover of a 12‐run Plackett‐Burman (PB) design, and further evaluated through a simulation study, in which the decoupling method is compared to existing analysis methods. To get a thorough understanding of the properties, both a PB design and an OMARS design are used, and different design sizes and heredity scenarios considered. The method is especially suited for screening, as it yields high power for detecting the active effects.
{"title":"A decoupling method for analyzing foldover designs","authors":"Yngvild Hole Hamre, John Sølve Tyssedal","doi":"10.1002/qre.3586","DOIUrl":"https://doi.org/10.1002/qre.3586","url":null,"abstract":"Foldover designs often have attractive properties. Among these is that the effects can be divided into two orthogonal subspaces, one for odd effects and one for even effects. In this paper, we introduce a new method for analyzing foldover designs called the decoupling method that exploits this trait. Utilizing mirror image pair runs, two new responses are created, where each of them is only affected by effects in one of the orthogonal subspaces. Thereby the analysis of odd and even effects can be performed in two independent steps, enabling use of standard statistical procedures and formal testing of the presence of higher‐order interactions. The method is demonstrated on real data from a foldover of a 12‐run Plackett‐Burman (PB) design, and further evaluated through a simulation study, in which the decoupling method is compared to existing analysis methods. To get a thorough understanding of the properties, both a PB design and an OMARS design are used, and different design sizes and heredity scenarios considered. The method is especially suited for screening, as it yields high power for detecting the active effects.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The hydraulic system is an integral part of CNC machine tools. In analyzing the reliability of machine tool hydraulic systems, their failures are influenced by both aleatory and epistemic uncertainties. This paper utilizes the fault tree analysis method to address failure modes subject to epistemic uncertainty, using the interval rough number scoring method to evaluate the probability of such failures occurring. The resulting reliability calculation is termed as “subjective reliability”. For failure modes influenced by aleatory uncertainty, objective data combined with the Dempster–Shafer evidence theory is used to determine their failure probability, with the corresponding reliability calculation referred to as “objective reliability”. Finally, a comprehensive calculation of both subjective and objective reliability is conducted to determine the overall reliability of the hydraulic system, along with the ranking of the importance of basic events of fault tree. This methodology covers scenarios with small samples, sufficient data, and their combinations, offering extensive application prospects.
{"title":"Evidence‐based fault tree analysis of the hydraulic system in CNC machine tools","authors":"Hong‐Xia Chen, Sui‐Xin Xie, Jun‐Feng Zhang, Wang‐Hao Chen, Bo Niu, Jiao‐Teng Zhang","doi":"10.1002/qre.3581","DOIUrl":"https://doi.org/10.1002/qre.3581","url":null,"abstract":"The hydraulic system is an integral part of CNC machine tools. In analyzing the reliability of machine tool hydraulic systems, their failures are influenced by both aleatory and epistemic uncertainties. This paper utilizes the fault tree analysis method to address failure modes subject to epistemic uncertainty, using the interval rough number scoring method to evaluate the probability of such failures occurring. The resulting reliability calculation is termed as “subjective reliability”. For failure modes influenced by aleatory uncertainty, objective data combined with the Dempster–Shafer evidence theory is used to determine their failure probability, with the corresponding reliability calculation referred to as “objective reliability”. Finally, a comprehensive calculation of both subjective and objective reliability is conducted to determine the overall reliability of the hydraulic system, along with the ranking of the importance of basic events of fault tree. This methodology covers scenarios with small samples, sufficient data, and their combinations, offering extensive application prospects.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liang Qu, Jin Li, Xiujie Zhao, Min Zhang, Zhenyu Lv
Step‐stress accelerated degradation test (SSADT) has become a prevailing approach to lifetime assessment for highly reliable products. In practice, many products suffer from multiple degradation processes that significantly contribute to failures. In this paper, we investigate the optimal SSADT plans for products subject to two dependent degradation characteristics modeled by a bivariate inverse Gaussian process. The drift parameter of each process is assumed to be influenced by a common stress factor. A bivariate Birnbaum‐Saunders (BVBS)‐type distribution is employed to approximate the lifetime distribution and facilitate the derivation of the objective function. The optimal plans are prescribed under three common optimality criteria in the presence of constraints on test units and inspections. A revisited example of fatigue crack is then presented to demonstrate the proposed methods. Finally, the sensitivity of the SSADT plans is studied, and the results exhibit fair robustness of the optimal plans.
{"title":"Optimal step stress accelerated degradation tests with the bivariate inverse Gaussian process","authors":"Liang Qu, Jin Li, Xiujie Zhao, Min Zhang, Zhenyu Lv","doi":"10.1002/qre.3583","DOIUrl":"https://doi.org/10.1002/qre.3583","url":null,"abstract":"Step‐stress accelerated degradation test (SSADT) has become a prevailing approach to lifetime assessment for highly reliable products. In practice, many products suffer from multiple degradation processes that significantly contribute to failures. In this paper, we investigate the optimal SSADT plans for products subject to two dependent degradation characteristics modeled by a bivariate inverse Gaussian process. The drift parameter of each process is assumed to be influenced by a common stress factor. A bivariate Birnbaum‐Saunders (BVBS)‐type distribution is employed to approximate the lifetime distribution and facilitate the derivation of the objective function. The optimal plans are prescribed under three common optimality criteria in the presence of constraints on test units and inspections. A revisited example of fatigue crack is then presented to demonstrate the proposed methods. Finally, the sensitivity of the SSADT plans is studied, and the results exhibit fair robustness of the optimal plans.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wind turbine blades are complex structures composed of multiple bonded components. The fatigue performance of these adhesive joints is crucial for ensuring operational safety over the blade's lifespan. Traditional structural fatigue analysis methods are inadequate for evaluating the fatigue properties of these joints due to the unique characteristics of adhesive materials. Variations in material and dimensional parameters, as well as fluctuating operational loads, further complicate the fatigue analysis of adhesive joints in wind turbine blades. To tackle this issue, this study introduces a fatigue analysis and reliability assessment method for the adhesive joints of wind turbine blades, employing the Cyclic Cohesive Zone Model (CCZM) and accounting for parameter uncertainties. Specifically, a novel methodology for fatigue analysis based on the CCZM is presented. The methodology is programmatically implemented to obtain a fatigue life dataset through multiple simulations, considering uncertainties in material parameters, adhesive dimensions, and loads. Subsequently, a fatigue reliability model is formulated to evaluate the fatigue reliability of adhesive joints in wind turbine blades under different parameter conditions, and the sensitivity of fatigue reliability to each parameter is investigated. The findings offer valuable insights for improving the safety and reliability of adhesive structures in wind turbine blades.
{"title":"CCZM‐based fatigue analysis and reliability assessment for wind turbine blade adhesive joints considering parameter uncertainties","authors":"Zheng Liu, Haodong Liu, Zhenjiang Shao, Jinlong Liang, Ruizhi Tang","doi":"10.1002/qre.3564","DOIUrl":"https://doi.org/10.1002/qre.3564","url":null,"abstract":"Wind turbine blades are complex structures composed of multiple bonded components. The fatigue performance of these adhesive joints is crucial for ensuring operational safety over the blade's lifespan. Traditional structural fatigue analysis methods are inadequate for evaluating the fatigue properties of these joints due to the unique characteristics of adhesive materials. Variations in material and dimensional parameters, as well as fluctuating operational loads, further complicate the fatigue analysis of adhesive joints in wind turbine blades. To tackle this issue, this study introduces a fatigue analysis and reliability assessment method for the adhesive joints of wind turbine blades, employing the Cyclic Cohesive Zone Model (CCZM) and accounting for parameter uncertainties. Specifically, a novel methodology for fatigue analysis based on the CCZM is presented. The methodology is programmatically implemented to obtain a fatigue life dataset through multiple simulations, considering uncertainties in material parameters, adhesive dimensions, and loads. Subsequently, a fatigue reliability model is formulated to evaluate the fatigue reliability of adhesive joints in wind turbine blades under different parameter conditions, and the sensitivity of fatigue reliability to each parameter is investigated. The findings offer valuable insights for improving the safety and reliability of adhesive structures in wind turbine blades.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonio Pérez‐Torres, René-Vinicio Sánchez, Susana Barceló‐Cerdá
Spur gearboxes are an integral component in the operation of rotary machines. Hence, the early determination of the severity level of a failure is crucial. This manuscript delineates a methodology for selecting essential mother wavelets and filters from the wavelet transform (WT) to process the vibration signal within the time‐frequency domain, aiming to ascertain the severity level of failures in spur gearboxes. Initially, information is garnered from the gearbox through vibration signals in the time domain, utilising six accelerometers. Subsequently, the signal is partitioned into various levels, and information from each level is extracted using diverse mother wavelets and their respective filters. The signal is segmented into sub‐bands, from which the condition state is ascertained using an energy operator. After that, the appropriate level of wave decomposition is determined through ANOVA tests and post‐hoc Tukey analyses, evaluating performance in failure classification via the Random Forest (RF) model. Upon establishing the decomposition level, the analysis proceeds to identify which mother wavelets and filters are most suitable for determining the severity level of different types of failure in spur gearboxes. Moreover, this study investigates the impact of sensor positioning and inclination on acquiring the vibration signal. This aspect is explored through factorial ANOVA tests and multiple comparisons of the data derived from the sensors. The RF classification model achieved exceedingly favourable results (accuracy 96% and AUC 98%), with minimal practical influence from the positioning and inclination of a sensor, thereby affirming the proposed methodology's suitability for this type of analysis.
{"title":"Selection of the level of vibration signal decomposition and mother wavelets to determine the level of failure severity in spur gearboxes","authors":"Antonio Pérez‐Torres, René-Vinicio Sánchez, Susana Barceló‐Cerdá","doi":"10.1002/qre.3578","DOIUrl":"https://doi.org/10.1002/qre.3578","url":null,"abstract":"Spur gearboxes are an integral component in the operation of rotary machines. Hence, the early determination of the severity level of a failure is crucial. This manuscript delineates a methodology for selecting essential mother wavelets and filters from the wavelet transform (WT) to process the vibration signal within the time‐frequency domain, aiming to ascertain the severity level of failures in spur gearboxes. Initially, information is garnered from the gearbox through vibration signals in the time domain, utilising six accelerometers. Subsequently, the signal is partitioned into various levels, and information from each level is extracted using diverse mother wavelets and their respective filters. The signal is segmented into sub‐bands, from which the condition state is ascertained using an energy operator. After that, the appropriate level of wave decomposition is determined through ANOVA tests and post‐hoc Tukey analyses, evaluating performance in failure classification via the Random Forest (RF) model. Upon establishing the decomposition level, the analysis proceeds to identify which mother wavelets and filters are most suitable for determining the severity level of different types of failure in spur gearboxes. Moreover, this study investigates the impact of sensor positioning and inclination on acquiring the vibration signal. This aspect is explored through factorial ANOVA tests and multiple comparisons of the data derived from the sensors. The RF classification model achieved exceedingly favourable results (accuracy 96% and AUC 98%), with minimal practical influence from the positioning and inclination of a sensor, thereby affirming the proposed methodology's suitability for this type of analysis.","PeriodicalId":56088,"journal":{"name":"Quality and Reliability Engineering International","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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