For rigid-flexible coupling multi-body with variable topology,such as the system of internally carried air-launched or heavy cargo airdrop,in order to construct a dynamic model with unified form,avoid redundancy in th...For rigid-flexible coupling multi-body with variable topology,such as the system of internally carried air-launched or heavy cargo airdrop,in order to construct a dynamic model with unified form,avoid redundancy in the modeling process and make the solution independent,a method based on the equivalent rigidization model was proposed.It divides a system into independent subsystems by cutting off the joints,of which types are changed with the operation process of the system.And models of different subsystems can be constructed via selecting suitable modeling methods.Subsystem models with flexible bodies are on the basis of the equivalent rigidization model which replaces the flexible bodies with the virtual rigid bodies.And the solution for sanction,which is based on the constraints force algorithm(CFA)and vector mechanics,can be independent on the state equations.The internally carried air-launched system was taken as an example for verifying validity and feasibility of the method and theory.The dynamic model of aircraft-rocket-parachute system in the entire phase was constructed.Comparing the modeling method with the others,the modeling process was programmed;and form of the model is unified and simple.The model,method and theory can be used to analyze other similar systems such as heavy cargo airdrop system and capsule parachute recovery system.展开更多
Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
文摘For rigid-flexible coupling multi-body with variable topology,such as the system of internally carried air-launched or heavy cargo airdrop,in order to construct a dynamic model with unified form,avoid redundancy in the modeling process and make the solution independent,a method based on the equivalent rigidization model was proposed.It divides a system into independent subsystems by cutting off the joints,of which types are changed with the operation process of the system.And models of different subsystems can be constructed via selecting suitable modeling methods.Subsystem models with flexible bodies are on the basis of the equivalent rigidization model which replaces the flexible bodies with the virtual rigid bodies.And the solution for sanction,which is based on the constraints force algorithm(CFA)and vector mechanics,can be independent on the state equations.The internally carried air-launched system was taken as an example for verifying validity and feasibility of the method and theory.The dynamic model of aircraft-rocket-parachute system in the entire phase was constructed.Comparing the modeling method with the others,the modeling process was programmed;and form of the model is unified and simple.The model,method and theory can be used to analyze other similar systems such as heavy cargo airdrop system and capsule parachute recovery system.
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
