Infrastructure deterioration poses significant challenges for asset management, yet existing approaches rely on population-averaged models that overlook equipment-specific heterogeneity. We present a novel framework that combines Bayesian hierarchical hazard modeling with causal discovery to identify operational patterns that drive heterogeneous deterioration rates in pump equipment. Our approach first estimates pump-specific random effects $u_i$ using GPU-accelerated No-U-Turn Sampling (NUTS), achieving 3--5$\times$ speedup over CPU implementations. We then employ DirectLiNGAM to discover causal relationships between 22 engineered time-series features and deterioration rates, stratified by positive ($u_i > 0$, faster deterioration) versus negative ($u_i \leq 0$, slower deterioration) random effects. Analyzing 112 pumps with 92,861 observations over 650 days, we uncover striking heterogeneity: the negative group exhibits causal effects 400$\times$ larger than the positive group, with standard deviation (std) showing a strong positive causal effect ($+1.515$) on deterioration rates in low-risk equipment. We validate linearity assumptions through NonlinearLiNGAM comparison and demonstrate practical scalability through GPU acceleration. Our findings enable targeted maintenance strategies by revealing that different operational regimes require fundamentally distinct management approaches, advancing predictive maintenance from population-averaged to heterogeneity-aware decision making.

翻译：基础设施劣化对资产管理构成重大挑战，然而现有方法依赖群体平均模型，忽略了设备特有的异质性。我们提出了一种新颖框架，将贝叶斯分层风险建模与因果发现相结合，以识别驱动泵设备异质性劣化率的运行模式。我们的方法首先使用GPU加速的No-U-Turn采样（NUTS）估计泵特定的随机效应$u_i$，相比CPU实现实现了3--5倍的加速。随后，我们采用DirectLiNGAM来发现22个工程化时间序列特征与劣化率之间的因果关系，并按正随机效应（$u_i > 0$，较快的劣化）与负随机效应（$u_i \leq 0$，较慢的劣化）进行分层。通过分析112台泵在650天内的92,861个观测值，我们揭示了显著的异质性：负效应组的因果效应比正效应组大400倍，其中标准差（std）对低风险设备的劣化率表现出强正向因果效应（$+1.515$）。我们通过与非线性LiNGAM比较验证了线性假设，并通过GPU加速展示了实际可扩展性。我们的研究结果通过揭示不同运行机制需要根本不同的管理策略，实现了从群体平均到异质性感知决策的预测性维护推进，从而支持针对性的维护策略。