Yonghong Liu

What is he best known for?

The fields of study he is best known for:

• Mechanical engineering
• Thermodynamics
• Electrical engineering

His primary areas of study are Metallurgy, Reliability engineering, Machining, Electrical discharge machining and Scanning electron microscope. His Reliability engineering study combines topics in areas such as Blowout preventer, Dynamic Bayesian network, Bayesian network and Subsea. His Machining research is multidisciplinary, relying on both Rotational speed, Electric discharge, Machine tool and Pulse generator.

His work carried out in the field of Electric discharge brings together such families of science as Silicon carbide and Ceramic. His study explores the link between Electrical discharge machining and topics such as Forensic engineering that cross with problems in Kerosene and Emulsion. His Scanning electron microscope research incorporates elements of Surface roughness, Titanium alloy and Crevice corrosion.

His most cited work include:

• Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network (186 citations)
• Study of the recast layer of a surface machined by sinking electrical discharge machining using water-in-oil emulsion as dielectric (107 citations)
• Investigation on the influence of the dielectrics on the material removal characteristics of EDM (87 citations)

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

Yonghong Liu mainly investigates Machining, Electrical discharge machining, Composite material, Metallurgy and Subsea. The Machining study combines topics in areas such as Engineering drawing, Silicon carbide, Ceramic, Surface roughness and Microstructure. While the research belongs to areas of Electrical discharge machining, Yonghong Liu spends his time largely on the problem of Dielectric, intersecting his research to questions surrounding Emulsion.

His study in Metallurgy is interdisciplinary in nature, drawing from both Rotational speed and Scanning electron microscope. His Subsea research includes elements of Blowout preventer, Reliability engineering and Sensitivity. His work deals with themes such as Control system and Dynamic Bayesian network, Bayesian network, which intersect with Reliability engineering.

He most often published in these fields:

• Machining (24.21%)
• Electrical discharge machining (23.68%)
• Composite material (18.42%)

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

• Composite material (18.42%)
• Dynamic Bayesian network (7.37%)
• Power (3.16%)

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

Yonghong Liu spends much of his time researching Composite material, Dynamic Bayesian network, Power, Reliability engineering and Mechanical engineering. Many of his research projects under Composite material are closely connected to Current density with Current density, tying the diverse disciplines of science together. Yonghong Liu combines subjects such as Algorithm and Deepwater drilling with his study of Dynamic Bayesian network.

His biological study spans a wide range of topics, including Well control, Process and Subsea. His Mechanical engineering research is multidisciplinary, incorporating perspectives in Development and Chip. His Orders of magnitude study spans across into subjects like Electrical discharge machining and Machining.

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
• Electrical engineering

Yonghong Liu focuses on Dynamic Bayesian network, Chemical engineering, Water splitting, Non-blocking I/O and Corrosion. The various areas that Yonghong Liu examines in his Dynamic Bayesian network study include Reliability engineering, Missing data, Subsea and Degradation. His work on Maintenance engineering as part of general Reliability engineering research is frequently linked to Data modeling, bridging the gap between disciplines.

Yonghong Liu usually deals with Subsea and limits it to topics linked to Absolute difference and Fault. His Chemical engineering study combines topics from a wide range of disciplines, such as Honeycomb structure, Honeycomb, Electrolysis and Nickel. Corrosion is a primary field of his research addressed under Composite material.