Welding quality diagnosis approaches with a normalized amplitude-invariant feature value

Abstract Intelligent diagnostic technology is helpful in the detection of welding quality issues which have a significant impact on product reliability, and attracts increasing concern. Welding diagnostic technology typically extracts feature values from raw signals and makes quality judgments based on these feature values. The conventional feature values extracted from electrical signals are not sensitive enough to indicate the welding quality and their amplitude varies with different welding powers, giving rise to difficulties in welding quality judgment. To address this problem, this paper proposes a normalized amplitude-invariant feature value (NAIFV), its extraction algorithm, as well as NAIFV-based threshold categorization (TC) and NAIFV-based support vector machine (SVM) for welding quality diagnosis. Experiments were conducted to validate the proposed approaches. It was found that NAIFV has advantages in rapidity, normalization and consistency compared to conventional feature values. Results also showed that the diagnosis accuracy of the NAIFV-based TC and NAIFV-based SVM reached 97.5% and 98.7% respectively, much higher than those based on spectral kurtosis, which is the best among the four conventional feature values discussed in this study.