With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagno...With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.展开更多
基金supported in part by the National Key R&D Program of China Grant 2022YFB4602200.
文摘With the intelligent upgrading of manufacturing equipment,achieving high-precision and efficient fault diagnosis is essential to enhance equipment stability and increase productivity.Online monitoring and fault diagnosis technology play a critical role in improving the stability of metal additive manufacturing equipment.However,the limited proportion of fault data during operation challenges the accuracy and efficiency of multi-classification models due to excessive redundant data.A multi-sensor and principal component analysis(PCA)and support vector machine(SVM)asymptotic classification(PCSV)for additive manufacturing fault diagnosis method is proposed,and it divides the fault diagnosis into two steps.In the first step,real-time data are evaluated using the T2 and Q statistical parameters of the PCAmodel to identify potential faults while filtering non-fault data,thereby reducing redundancy and enhancing real-time efficiency.In the second step,the identified fault data are input into the SVM model for precise multi-class classification of fault categories.The PCSV method advances the field by significantly improving diagnostic accuracy and efficiency,achieving an accuracy of 99%,a diagnosis time of 0.65 s,and a training time of 503 s.The experimental results demonstrate the sophistication of the PCSV method for high-precision and high-efficiency fault diagnosis of small fault samples.
