Predictive maintenance in manufacturing environments presents a challenging optimization problem characterized by extreme cost asymmetry, where missed failures incur costs roughly fifty times higher than false alarms. Predictive maintenance in manufacturing environments presents a challenging optimization problem characterized by extreme cost asymmetry, where missed failures incur costs roughly fifty times higher than false alarms. Conventional machine learning approaches typically optimize statistical accuracy metrics that do not reflect this operational reality and cannot reliably distinguish causal relationships from spurious correlations. This study benchmarks eight predictive models, ranging from baseline statistical approaches to Bayesian structural causal methods, on a dataset of 10,000 CNC machines with a 3.3 percent failure prevalence. While ensemble correlation-based models such as Random Forest (L4) achieve the highest raw cost savings (70.8 percent reduction), the Bayesian Structural Causal Model (L7) delivers competitive financial performance (66.4 percent cost reduction) with an inherent ability of failure attribution, which correlation-based models do not readily provide. The model achieves perfect attribution for HDF, PWF, and OSF failure types. These results suggest that causal methods, when combined with domain knowledge and Bayesian inference, offer a potentially favorable trade-off between predictive performance and operational interpretability in predictive maintenance applications.

翻译：制造业环境中的预测性维护呈现出一个具有挑战性的优化问题，其特点是极端的成本不对称性，其中漏检故障所产生的成本大约是误报成本的五十倍。传统的机器学习方法通常优化的是统计准确性指标，这些指标并不能反映这种操作现实，且无法可靠地区分因果关系与虚假相关性。本研究在包含10,000台CNC机床（故障发生率为3.3%）的数据集上，对八种预测模型进行了基准测试，这些模型涵盖了从基线统计方法到贝叶斯结构因果方法的范围。虽然基于相关性的集成模型（如随机森林，L4）实现了最高的原始成本节约（成本降低70.8%），但贝叶斯结构因果模型（L7）提供了具有竞争力的财务性能（成本降低66.4%），并具备故障归因的内在能力，这是基于相关性的模型所不易提供的。该模型对HDF、PWF和OSF故障类型实现了完美的归因。这些结果表明，在预测性维护应用中，因果方法与领域知识和贝叶斯推理相结合，在预测性能和操作可解释性之间提供了潜在更优的权衡。