Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken us...Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken using component failure rate data to predict PTC for a full stroke test and a partial stroke test.Given the subjective and uncertain aspects of the FMEDA approach,specifically the selection of component failure rates and the determination of the probability of detecting failure modes,a Fuzzy Inference System(FIS)was proposed to manage the data,addressing the inherent uncertainties.Fuzzy inference systems have been used previously for various FMEA type assessments,but this is the first time an FIS has been employed for use with FMEDA.ESDV PTC values were generated from both the standard FMEDA and the fuzzy-FMEDA approaches using data provided by FMEDA experts.This work demonstrates that fuzzy inference systems can address the subjectivity inherent in FMEDA data,enabling reliable estimates of ESDV proof test coverage for both full and partial stroke tests.This facilitates optimized maintenance planning while ensuring safety is not compromised.展开更多
A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It use...A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning) is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.展开更多
Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation,site selection,and planning in areas prone to multiple natural hazards.In this study,we proposed a ...Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation,site selection,and planning in areas prone to multiple natural hazards.In this study,we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide,flood,and earthquake hazard assessments for a basin in Elazig Province,Türkiye.To produce the landslide susceptibility map,an ensemble machine learning algorithm,random forest,was chosen because of its known performance in similar studies.The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study.The seismic hazard was assessed using ground motion parameters based on Arias intensity values.The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert.The results show that the random forest provided an overall accuracy of 92.3%for landslide susceptibility mapping.Of the study area,41.24%were found prone to multi-hazards(probability value>50%),but the southern parts of the study area are more susceptible.The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.展开更多
The use of artificial intelligence models in predicting the moisture content reduction in the drying of potato(Ipomoea batata)sliceswas the focus of thiswork.The models used were adaptive neuro fuzzy inference systems...The use of artificial intelligence models in predicting the moisture content reduction in the drying of potato(Ipomoea batata)sliceswas the focus of thiswork.The models used were adaptive neuro fuzzy inference systems(ANFIS),artificial neural network(ANN)and response surface methodology(RSM).The parameters considered were drying time,drying air speed and temperature.The capability and sensitivity analysis of the three models were evaluated using the correlation coefficient(R2)and some statistical error functions such as the average relative error(ARE),root mean square error(RMSE),Hybrid Fractional Error Function(HYBRID)and absolute average relative error(AARE).The result showed that the three models demonstrated significant predictive behaviourwith R2 of 0.998,0.997 and 0.998 for ANFIS,ANN and RSMrespectively.The calculated error functions of ARE(RSM=1.778,ANFIS=1.665 and ANN=4.282)and RMSE(RSM=0.0273,ANFIS=0.0282 and ANN=0.1178)suggested good harmony between the experimental and predicted values.It was concluded that though the three models gave adequate predictions that were in good agreement with the experimental data,the RSM and ANFIS gave better model prediction than ANN.展开更多
文摘Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken using component failure rate data to predict PTC for a full stroke test and a partial stroke test.Given the subjective and uncertain aspects of the FMEDA approach,specifically the selection of component failure rates and the determination of the probability of detecting failure modes,a Fuzzy Inference System(FIS)was proposed to manage the data,addressing the inherent uncertainties.Fuzzy inference systems have been used previously for various FMEA type assessments,but this is the first time an FIS has been employed for use with FMEDA.ESDV PTC values were generated from both the standard FMEDA and the fuzzy-FMEDA approaches using data provided by FMEDA experts.This work demonstrates that fuzzy inference systems can address the subjectivity inherent in FMEDA data,enabling reliable estimates of ESDV proof test coverage for both full and partial stroke tests.This facilitates optimized maintenance planning while ensuring safety is not compromised.
文摘A two-phase approach to fuzzy system identification is proposed. The first phase produces a baseline design to identify a prototype fuzzy system for a target system from a collection of input-output data pairs. It uses two easily implemented clustering techniques: the subtractive clustering method and the fuzzy c-means (FCM) clustering algorithm. The second phase (fine tuning) is executed to adjust the parameters identified in the baseline design. This phase uses the steepest descent and recursive least-squares estimation methods. The proposed approach is validated by applying it to both a function approximation type of problem and a classification type of problem. An analysis of the learning behavior of the proposed approach for the two test problems is conducted for further confirmation.
文摘Preparation of accurate and up-to-date susceptibility maps at the regional scale is mandatory for disaster mitigation,site selection,and planning in areas prone to multiple natural hazards.In this study,we proposed a novel multi-hazard susceptibility assessment approach that combines expert-based and supervised machine learning methods for landslide,flood,and earthquake hazard assessments for a basin in Elazig Province,Türkiye.To produce the landslide susceptibility map,an ensemble machine learning algorithm,random forest,was chosen because of its known performance in similar studies.The modified analytical hierarchical process method was used to produce the flood susceptibility map by using factor scores that were defined specifically for the area in the study.The seismic hazard was assessed using ground motion parameters based on Arias intensity values.The univariate maps were synthesized with a Mamdani fuzzy inference system using membership functions designated by expert.The results show that the random forest provided an overall accuracy of 92.3%for landslide susceptibility mapping.Of the study area,41.24%were found prone to multi-hazards(probability value>50%),but the southern parts of the study area are more susceptible.The proposed model is suitable for multi-hazard susceptibility assessment at a regional scale although expert intervention may be required for optimizing the algorithms.
文摘The use of artificial intelligence models in predicting the moisture content reduction in the drying of potato(Ipomoea batata)sliceswas the focus of thiswork.The models used were adaptive neuro fuzzy inference systems(ANFIS),artificial neural network(ANN)and response surface methodology(RSM).The parameters considered were drying time,drying air speed and temperature.The capability and sensitivity analysis of the three models were evaluated using the correlation coefficient(R2)and some statistical error functions such as the average relative error(ARE),root mean square error(RMSE),Hybrid Fractional Error Function(HYBRID)and absolute average relative error(AARE).The result showed that the three models demonstrated significant predictive behaviourwith R2 of 0.998,0.997 and 0.998 for ANFIS,ANN and RSMrespectively.The calculated error functions of ARE(RSM=1.778,ANFIS=1.665 and ANN=4.282)and RMSE(RSM=0.0273,ANFIS=0.0282 and ANN=0.1178)suggested good harmony between the experimental and predicted values.It was concluded that though the three models gave adequate predictions that were in good agreement with the experimental data,the RSM and ANFIS gave better model prediction than ANN.
