Survival Analysis of the Compressor Station Based on Hawkes Process with Weibull Base Intensity

by   Lu-ning Zhang, et al.

In this paper, we use the Hawkes process to model the sequence of failure, i.e., events of compressor station and conduct survival analysis on various failure events of the compressor station. However, until now, nearly all relevant literatures of the Hawkes point processes assume 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 base intensity of the truth conditional intensity function is very likely to be time-varying. The constant base intensity will not reflect the base probability of the failure occurring over time. Thus, in order to solve this problem, in this paper, we propose a new time-varying base intensity, for example, which is from Weibull distribution. First, we introduce the base intensity from the Weibull distribution, and then we propose an effective learning algorithm by 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 the real-world data reveal the Granger causality of different kinds of failures and the base probability of failure varying over time.


Wasserstein Learning of Deep Generative Point Process Models

Point processes are becoming very popular in modeling asynchronous seque...

Bayesian Reliability Analysis of the Power Law Process with Respect to the Higgins-Tsokos Loss Function for Modeling Software Failure Times

The Power Law Process, also known as Non-Homogeneous Poisson Process, ha...

Learning Granger Causality for Hawkes Processes

Learning Granger causality for general point processes is a very challen...

Multi-output Gaussian Process Modulated Poisson Processes for Event Prediction

Prediction of events such as part replacement and failure events plays a...

Inferring the time-varying functional connectivity of large-scale computer networks from emitted events

We consider the problem of inferring the functional connectivity of a la...

Short-term Temporal Dependency Detection under Heterogeneous Event Dynamic with Hawkes Processes

Many event sequence data exhibit mutually exciting or inhibiting pattern...

Inhomogeneous Markov Survival Regression Models

We propose new regression models in survival analysis based on homogeneo...

Please sign up or login with your details

Forgot password? Click here to reset