A Cost-Sensitive Transformer Model for Prognostics Under Highly
  Imbalanced Industrial Data
Ali Beikmohammadi Mohammad Hosein Hamian Neda Khoeyniha Tony Lindgren Olof Steinert Sindri Magnússon
The rapid influx of data-driven models into the industrial sector has been
facilitated by the proliferation of sensor technology, enabling the collection
of vast quantities of data. However, leveraging these models for failure
detection and prognosis poses significant challenges, including issues like
missing values and class imbalances. Moreover, the cost sensitivity associated
with industrial operations further complicates the application of conventional
models in this context. This paper introduces a novel cost-sensitive
transformer model developed as part of a systematic workflow, which also
integrates a hybrid resampler and a regression-based imputer. After subjecting
our approach to rigorous testing using the APS failure dataset from Scania
trucks and the SECOM dataset, we observed a substantial enhancement in
performance compared to state-of-the-art methods. Moreover, we conduct an
ablation study to analyze the contributions of different components in our
proposed method. Our findings highlight the potential of our method in
addressing the unique challenges of failure prediction in industrial settings,
thereby contributing to enhanced reliability and efficiency in industrial
operations.