H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology D-index 30 Citations 3,862 120 World Ranking 6964 National Ranking 844

Overview

What is he best known for?

The fields of study he is best known for:

  • Mechanical engineering
  • Thermodynamics
  • Composite material

His primary areas of study are Reliability engineering, Bayesian network, Probabilistic logic, Data mining and Artificial intelligence. Baoping Cai has researched Reliability engineering in several fields, including Power grid and Dynamic Bayesian network. He combines topics linked to Subsea with his work on Bayesian network.

His research on Probabilistic logic often connects related areas such as Knowledge representation and reasoning. His Data mining research is multidisciplinary, incorporating elements of Blowout preventer, Closing, Correctness and Sensitivity. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Reduction.

His most cited work include:

  • Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network (186 citations)
  • A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems (169 citations)
  • Bayesian Networks in Fault Diagnosis (168 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Reliability engineering, Bayesian network, Subsea, Dynamic Bayesian network and Machining. In general Reliability engineering, his work in Resilience and Failure rate is often linked to Reliability linking many areas of study. His work carried out in the field of Bayesian network brings together such families of science as Probabilistic logic, Mutual information, Data mining and Process.

His Probabilistic logic research incorporates elements of Machine learning and Knowledge representation and reasoning. His Subsea research focuses on subjects like Blowout preventer, which are linked to Triple modular redundancy. His Machining study combines topics from a wide range of disciplines, such as Surface roughness, Microstructure, Engineering drawing and Ceramic.

He most often published in these fields:

  • Reliability engineering (32.56%)
  • Bayesian network (23.26%)
  • Subsea (20.16%)

What were the highlights of his more recent work (between 2019-2021)?

  • Reliability engineering (32.56%)
  • Dynamic Bayesian network (19.38%)
  • Subsea (20.16%)

In recent papers he was focusing on the following fields of study:

Baoping Cai mainly investigates Reliability engineering, Dynamic Bayesian network, Subsea, Corrosion and Submarine pipeline. His Failure rate and Resilience study, which is part of a larger body of work in Reliability engineering, is frequently linked to Service, Service networks and Cascading failure, bridging the gap between disciplines. His studies in Dynamic Bayesian network integrate themes in fields like Algorithm and Process.

His study on Process also encompasses disciplines like

  • Markov chain that connect with fields like Bayesian network,
  • Fault tree analysis and related Well control. He connects Bayesian network with Fault detection and isolation in his research. His research in Subsea intersects with topics in Preventive maintenance, Particle swarm optimization, Absolute difference and Offshore oil and gas.

Between 2019 and 2021, his most popular works were:

  • Remaining Useful Life Estimation of Structure Systems Under the Influence of Multiple Causes: Subsea Pipelines as a Case Study (55 citations)
  • Remaining useful life re-prediction methodology based on Wiener process: Subsea Christmas tree system as a case study (11 citations)
  • A dynamic Bayesian network based methodology for fault diagnosis of subsea Christmas tree (11 citations)

In his most recent research, the most cited papers focused on:

  • Mechanical engineering
  • Thermodynamics
  • Composite material

Baoping Cai mostly deals with Dynamic Bayesian network, Corrosion, Water splitting, Non-blocking I/O and Chemical engineering. His Dynamic Bayesian network research is multidisciplinary, incorporating perspectives in Reliability engineering, Missing data, Subsea and Degradation. His Reliability engineering research integrates issues from Safety valve and Absolute difference.

Baoping Cai combines subjects such as Pipeline transport, Point, Maintenance engineering and Offshore oil and gas with his study of Subsea. Baoping Cai has included themes like Coating and Microstructure in his Corrosion study. His Composite material study frequently intersects with other fields, such as Nanoparticle.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network

Baoping Cai;Yonghong Liu;Qian Fan;Yunwei Zhang.
Applied Energy (2014)

258 Citations

A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks

Baoping Cai;Baoping Cai;Hanlin Liu;Min Xie.
Mechanical Systems and Signal Processing (2016)

202 Citations

Bayesian Networks in Fault Diagnosis

Baoping Cai;Lei Huang;Min Xie.
IEEE Transactions on Industrial Informatics (2017)

190 Citations

A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

Baoping Cai;Yubin Zhao;Hanlin Liu;Min Xie.
IEEE Transactions on Power Electronics (2017)

179 Citations

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

Baoping Cai;Yu Liu;Min Xie.
IEEE Transactions on Automation Science and Engineering (2017)

154 Citations

Study of the recast layer of a surface machined by sinking electrical discharge machining using water-in-oil emulsion as dielectric

Yanzhen Zhang;Yonghong Liu;Renjie Ji;Baoping Cai.
Applied Surface Science (2011)

145 Citations

Availability-Based Engineering Resilience Metric and Its Corresponding Evaluation Methodology

Baoping Cai;Baoping Cai;Min Xie;Yonghong Liu;Yiliu Liu.
Reliability Engineering & System Safety (2018)

124 Citations

Using Bayesian networks in reliability evaluation for subsea blowout preventer control system

Baoping Cai;Yonghong Liu;Zengkai Liu;Xiaojie Tian.
Reliability Engineering & System Safety (2012)

118 Citations

A dynamic Bayesian networks modeling of human factors on offshore blowouts

Baoping Cai;Yonghong Liu;Yunwei Zhang;Qian Fan.
Journal of Loss Prevention in The Process Industries (2013)

115 Citations

Application of Bayesian networks in quantitative risk assessment of subsea blowout preventer operations.

Baoping Cai;Yonghong Liu;Zengkai Liu;Xiaojie Tian.
Risk Analysis (2013)

106 Citations

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