Research On Condition Monitoring And Fault Diagnosis Methods And Applications For Hydraulic System

Hydraulic transmission and control technology is wildly applied in national economy.In order to ensure the reliable operation of the equipment and improve the working efficiency,it is great significance to research the condition monitoring and fault diagnosis technology of the hydraulic system.Due to the complex application scenarios of hydraulic system,especially in the high-frequency field,the system condition signal is usually be interfered various factors.The result in the collected value is deviating from the real value,which makes it difficult to implement effective condition monitoring.On the other hand,hydraulic system is a comprehensive system of machine,electricity and fluid with complex structures.The faults of hydraulic system have following features:concealed location,the fault mechanism is unclear and otherness.Different faults have the same signal features,and the general theoretical fault features are difficult to be obtained.So that existing diagnostic methods are difficult to effectively detect hydraulic system faults.Therefore,the development of effective condition monitoring andidentification methods is the unremitting pursuit of the research in the field of hydraulic system fault diagnosis.In this paper,a high-pass filter enhanced EEMD(Ensemble Empirical Mode Decomposition)method is proposed for hydraulic system condition monitoring.In addition,a DSm-theory-based hybrid-intelligent identification method is proposed to detect faults in hydraulic systems,which can successfully diagnose 12 fault types and locations in hydraulic valves.The main work and research contents of this paper are as follows:Hydraulic system is easily interfered by many factors and presents non-stationary features in high frequency filed.Aiming at solving this problem,a high-pass filter enhanced EEMD method is proposed for hydraulic system condition monitoring.In order to verify the effectiveness of the proposed method,a high frequency hydraulic vibrator is developed for experimental study.In the experimental study,high-pass filter is used to remove some known noise components,and EEMD is used to further process the signal to obtain anti-interference condition feedback signal.Aiming at solving the problem that the unclear fault mechanism of complex hydraulic system fault and the theory fault features are difficult to be obtained,a hybrid intelligent fault diagnosis method based on DSm theory is proposed.This method can automatically learn fault features through faults data and not need prior knowledge.Experimentalinvestigations are performed to validate the performance of the present method using a hydraulic valve controlled hydraulic test rig.The results show that the recognition accuracy of 12 types is 98.1%,which are better than other methods.