In this paper, we construct a Hawkes process with time-varying base intensity to model the sequence of failure, i.e., failure events of the compressor station, and we combine survival analysis and point process model on various failure events of the compressor station based on Hawkes process. To our best knowledge, until now, nearly all relevant literature of the Hawkes point processes assumes that the base intensity of the conditional intensity function is time-invariant. This assumption is apparently too harsh to be verified. For example, in the practical application, including financial analysis, reliability analysis, survival analysis and social network analysis, the truth variation of the base intensity of the failure occurrence over time is not constant. The constant base intensity will not reflect the base intensity trend of the failure occurring over time. Thus, in order to solve this problem, in this paper, we propose a new time-varying base intensity, e.g. which is treated as obeying Weibull distribution. First, we introduce the base intensity into a Hawkes process that obeys the Weibull distribution, and then we propose an effective learning algorithm based on the maximum likelihood estimator. Experiments on the constant base intensity synthetic data, time-varying base intensity synthetic data, and real-world data show that our method can learn the triggering patterns of the Hawkes processes and the time-varying base intensity simultaneously and robustly. Experiments on real-world data also reveal the Granger causality of different types of failures and the base probability of failure varying over time. We put forward some suggestions for practical production based on the experimental results.

在本文中，我们建立了一个时基强度随时间变化的霍克斯过程，以对故障序列（即压缩机站的故障事件）进行建模，并基于该模型对压缩机站的各种故障事件进行了生存分析和点过程模型的结合。霍克斯过程。据我们所知，到目前为止，霍克斯点过程的几乎所有相关文献都假定条件强度函数的基本强度是时不变的。这个假设显然太苛刻，无法验证。例如，在实际应用中，包括财务分析，可靠性分析，生存分析和社交网络分析，故障发生的基本强度随时间的真实变化不是恒定的。恒定的基本强度不会反映随时间推移发生的故障的基本强度趋势。因此，为了解决这个问题，本文提出了一种新的随时间变化的基本强度，例如，服从威布尔分布。首先，将基本强度引入服从Weibull分布的Hawkes过程中，然后提出基于最大似然估计器的有效学习算法。对恒定基础强度合成数据，随时间变化的基础强度合成数据和真实世界数据进行的实验表明，我们的方法可以同时且鲁棒地学习霍克斯过程的触发模式和随时间变化的基础强度。实际数据的实验还揭示了不同类型故障的格兰杰因果关系，以及故障的基本概率随时间变化。根据实验结果，我们对实际生产提出了一些建议。