Gene

Lightning arrester

Bayesian probability

Operating system

Electrical engineering

Machine learning

Physics

Data mining

Engineering

Physical chemistry

Artificial intelligence

Power (physics)

Lightning strike

Chemistry

Reliability engineering

Overhead (engineering)

Bayesian network

Computer science

Probabilistic logic

Biochemistry

Quantum mechanics

Robustness (evolution)

Lightning (connector)

Fragility

Reliability Engineering & System Safety

Lightning-related failures are of great concerns for the reliable performance of overhead contact lines (OCLs) of high-speed railway. Predicting lightning-related failure probability is valuable to capture the recurrence of OCL failures due to lightning strike and enable predictive maintenance decision-making. In this paper, a data-driven Bayesian network (BN) approach with spatiotemporal fragility model is developed to investigate the dependencies between lightning strike and OCL failures, and predict lightning-related failure risk of OCLs. It consists of three critical components, (1) a probabilistic lightning model that integrates multiple key lightning parameters is proposed to capture the uncertainty in the occurrence and intensity of lightning strike; (2) a spatiotemporal fragility model of OCL corridor is presented to examine the impacts of lightning strike on OCL failure probability; (3) furthermore, the Bayesian network is embedded with above-mentioned two models to predict lightning-related failure risk of OCLs, improving its robustness. Compared with other advanced prediction methods, the proposed approach achieves better prediction performance with high accuracy over imbalanced dataset. In addition, it can still work acceptably on noisy lightning data with a signal-to-noise ratio of 15dB or higher.

Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model