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.

该文介绍了一种新颖的成本敏感变压器模型，该模型通过融合混合型采样器和基于回归的填充器，以及经过严格测试的方法，从而在工业环境中预测故障方面表现出比现有方法更好的性能，并分析了所提出方法中不同组成部分的贡献。研究结果凸显了该方法在解决工业设置中故障预测的独特挑战方面的潜力，从而提高了工业操作的可靠性和效率。