Titlebook: Conference Proceedings of 2021 International Joint Conference on Energy, Electrical and Power Engine; Component Design, Op Wenping Cao,Cung

预知

https://doi.org/10.1007/978-1-61779-376-9The disassembly analysis of the main components is used to comprehensively evaluate the overall performance of the new hybrid engine. Finally, the test engine meets the test requirements, with good performance and no major failures, which provides a reference value for future development and improvement of the endurance test of the hybrid engine.

acclimate

Chromatin Affinity Purificationnd selection principles of different types of phase-shifting transformers are analyzed. Combined with the development and application status of PST’s technology, the development prospect of PST is discussed.

特征

Brandon W. Hughes,Eric J. Nestleragnetic flux leakage signals of various states are obtained. The neural network model is used to process the magnetic flux leakage signals and determine the fault phase in order to achieve the purpose of detection and diagnosis. Compared with traditional MCSA, MFL detection method has the advantages of robustness and non-invasion.

Diverticulitis

Cubicle

A Novel Ontological Structure Design for Power Transformer Considering Magnetostriction and Jointsogical structure design with the core and windings position interchange is put forward. The testing results of the prototype show that it is an effective means to reduce vibration and acoustic noise of power transformer.

通情达理

Experimental Study and Analysis on Durability of a Hybrid EngineThe disassembly analysis of the main components is used to comprehensively evaluate the overall performance of the new hybrid engine. Finally, the test engine meets the test requirements, with good performance and no major failures, which provides a reference value for future development and improvement of the endurance test of the hybrid engine.

Brain-Waves

召集

Application of Finite Element Magnetic Flux Leakage Simulation in Fault Detection of Brushless DC Moagnetic flux leakage signals of various states are obtained. The neural network model is used to process the magnetic flux leakage signals and determine the fault phase in order to achieve the purpose of detection and diagnosis. Compared with traditional MCSA, MFL detection method has the advantages of robustness and non-invasion.