Zengkai Liu

TL;DR: The results show that the fault diagnosis model using evidences from only sensor data is accurate for single fault, while it is not accurate enough for multiple-simultaneous faults, and the multi-source information fusion based fault diagnosed model using Bayesian network can increase the fault diagnostic accuracy greatly.

TL;DR: An application of dynamic Bayesian networks for quantitative risk assessment of human factors on offshore blowouts is presented and the results show that the human factor barrier failure probability only increases within the first two weeks and rapidly reaches a stable level when the repair is considered.

TL;DR: The results show that the DDMR control system has a little higher reliability than TMR system, and the component failure rates of Ethernet switch, programmable logic controller and personal computer (PC) and PC should be reduced for DDMR system.

TL;DR: A methodology for the application of Bayesian networks in conducting quantitative risk assessment of operations in offshore oil and gas industry and the results show that mechanical and hydraulic factors have the most important effects on operation safety.

TL;DR: Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds.