Benchmark for welding gun fault prediction with multivariate time series data.

ABSTRACT: In the automotive industry, machinery failures of the resistance spot welding (RSW) guns would interrupt the manufacturing lines and cause unplanned downtime, potentially resulting in a significant loss of production and reliability. Predicting the machinery failures of the RSW gun can provide more scientific strategies for predictive maintenance and decision-making. However, fault prediction of RSW guns has become increasingly challenging due to their complex behavior and data variability. In this paper, we created a benchmark dataset and proposed welding gun fault prediction benchmarks to aid in the development of machine learning approaches toward welding gun fault prediction. The dataset was collected at the Body-Shop (BS) of BMW Brilliance Automotive Ltd. from different components of

SUBMITTER: Wang X 

PROVIDER: S-EPMC10796372 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature