Research On Fault Diagnosis And Health Monitoring Of Hydraulic Excavator

Hydraulic excavator is one of the most common construction machinery,both in the field of engineering construction and production life have been widely used,such as highways,railroads,mines,etc.From the development trend of construction machinery in recent years,the excavator has developed relatively rapidly,both in terms of production and technical level have been greatly improved.Hydraulic excavator has a background of hydraulic,mechanical,electronic and other fields of technology integration,with the improvement of the level of technology in each field is towards more diverse and intelligent functions,more complex and compact structure trends,at the same time the probability of failure will also be increased,excavator failure occurs with multiple concurrency,network-like diffusion,fuzzy characterization,troubleshooting difficulties and other characteristics.Once the excavator has some kind of failure,it will lead to lower efficiency and lower economic benefits,or threaten personal and property safety.Therefore,in order to ensure the working performance of excavators and avoid losses caused by faults,the performance status of excavators must be monitored in real time.In order to accurately sense the health status of the excavator hydraulic system and avoid the loss of economic benefits and personnel injury when a failure occurs during operation,this paper conducts research on the failure mode mechanism,failure mode diagnosis,and failure level identification for the key hydraulic components of the excavator hydraulic system,so as to realize the fault diagnosis and health monitoring of the hydraulic excavator and develop a health management platform to realize the remote operation and maintenance of the excavator,and the main research contents are as follows:(1)Common failure modes of excavator hydraulic system and the expression form of failure are concluded,and common failure mechanism of key hydraulic components of hydraulic system is analyzed.The condition sensing scheme of the hydraulic excavator is designed,the test excavator is modified and installed with the sensors and hydraulic valves required for the test,and the signals of vibration,temperature,pressure and displacement of the excavator under different working conditions are collected as the data sources for subsequent fault diagnosis as well as health monitoring.(2)For the leakage failure of hydraulic cylinder,experiments on three leakage modes of hydraulic cylinder(external leakage with rod cavity,external leakage without rod cavity and internal leakage),as well as four leakage levels(no leakage,mild leakage,medium leakage and severe leakage)were carried out.The algorithm of hydraulic cylinder leakage diagnosis based on cyclostationary features and Stacking model is proposed,and the hydraulic cylinder leakage diagnosis is completed by extracting cyclic smooth features into the Stacking model with the outlet pressure signal as the analysis signal.The comparison with other integrated learning algorithms shows that the proposed method has higher accuracy in the evaluation results.(3)For the fault diagnosis of excavator plunger pump,a special fault test bench of plunger pump is built,and the fault simulation test of piston pump is designed according to the fault mechanism.The fault diagnosis method of the piston pump based on the variational modal decomposition and XGBoost model is proposed.The pump casing vibration signal is used as the analysis signal,from which the fault features with high resolution are extracted,and the fault features are input into the model to realize the fault mode diagnosis and fault level identification.The results show that the proposed method has a high recognition accuracy and excellent noise immunity.(4)Based on the above research,the remote operation and maintenance platform for excavator fault diagnosis and health monitoring is developed,including the hardware design of the data acquisition system and equipment gateway and the software design of the operation and maintenance platform website,which improves the intelligence level of hydraulic excavator health management.