首页|Studies from Technical University Munich (TU Munich) Further Understanding of Ma chine Learning (Development of Machine Learning Based Classifier for the Pressur e Test Result Prediction of Type Iv Composite Overwrapped Pressure Vessels)

Studies from Technical University Munich (TU Munich) Further Understanding of Ma chine Learning (Development of Machine Learning Based Classifier for the Pressur e Test Result Prediction of Type Iv Composite Overwrapped Pressure Vessels)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Garchi ng,Germany,by NewsRx journalists,research stated,"The stringent safety regul ations of type IV composite overwrapped pressure vessels (COPVs) for commercial vehicles mandate a certification process involving pressurization up to 1050 bar ,with the critical requirement of withstanding burst pressures of 1570 bar. Ana lyzing proof test data is crucial to enhance and ensure tank safety regarding bu rst pressure." The news reporters obtained a quote from the research from Technical University Munich (TU Munich),"In this study,we developed various machine learning classi fiers for structure health monitoring and damage prediction of COPVs. The classi fiers were trained using a substantial amount of acoustic emission data collecte d during burst and pressure cycling tests. The test results were employed as lab el inputs during the training process. Statistical features were extracted per t ime unit and trained using Naive Bayes,Logistic Regression,Decision Tree,XGBo ost,and TabNet models. Upon training the data collected from the burst pressure test,TabNet,Decision Tree,and XGBoost achieved classification accuracies abo ve 0.94. Notably,TabNet demonstrated also the best performance for the pressure cycling test with an accuracy of 0.98. Furthermore,TabNet provided visualizati ons of feature sensitivity in relation to classification results." According to the news reporters,the research concluded: "This study marks the f irst development of a machine learning classifier for predicting the damage stat e of COPV tanks in commercial applications pertaining to required safety tests."

GarchingGermanyEuropeCyborgsEmer ging TechnologiesMachine LearningTechnical University Munich (TU Munich)

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.29)