Fault Diagnosis And Preventive Maintenance Optimization Of The Dump Truck Chassis Based On Vibration Modal Recognition

Driven by the economic development and accelerated economic transformation,China’s logistics industry plays a decisive role in the economic development.Municipal construction,urban and rural development,bridge engineering and key projects continue to accelerate the pace of construction,to promote dump truck sales and operation ushered in the explosive development.Due to the harsh operating environment,complex and changeable operating conditions,dump truck chassis system often occurs many kinds of failures,affects the logistics fleet operational availability and reliability,and even endanger the property and personal safety,resulting in increased vehicle maintenance cost.Diagnosing and identifying early failure and timely maintenance service can effectively reduce the probability of failure and accident,to ensure vehicle safety and availability.If it ispassable to set up a preventive maintenance plan according to the fault diagnosis result,it is of great practical significance to improve the operational efficiency and reduce the operating cost of the commercial fleet.As a modal recognition algorithm,the stochastic subspace method has many advantages including lower requirement of testing hardware,higher recognition precision and being suitable for stochastic vibration.However,its recognition accuracy can be significantly affected by the damping ratio,whichin turn limits its application in the monitoring anddiagnosisdump truck system,since the damping ratio ofdump truck suspension system is relatively high(up to20%-30%).Therefore,this study proposes an improved average correlation subspace method-stochastic subspace identification(ACS-SSI)and then applies it to the on-line monitoring of the suspension.The multiple average correlation function signal is used to replace the original response signal as the algorithm input,which significantly improves the identification accuracy of subspace diagnostic algorithm under complex operating conditions.In order to verify the effectiveness of the ACS-SSI in the condition monitoring and fault diagnosis of the dump truck chassis system,a seven-degree-of-freedom model and an eleven-degree-of-freedom vibration model were established.The sensitivity of each mode parameter to the fault event is judged,and the online monitoring method based on the vibration mode and modal energy difference method is established based on this judgment.Finally,the feasibility of the on-line fault diagnosis method is verified by the on-road vehicle test.The fault identification method based on stochastic subspace algorithm can effectively diagnose the early failure of dump truck chassis,but it is difficult to give an accurate judgment on the source of the specific fault types and the coupling of multiple faults.This paper proposes a concept of time-varying bottom event weight coefficients based on statistical maintenance data,and builds an improved fault tree analysis model for dump truck chassis.Due to thenon-stationary operating conditions,the failure probability of dump truck chassis system not only follow the traditional "bathtub curve",but also include some kind of annual variation.Thus,it is necessary to combine the results of online fault diagnosis and nonlinearfailure rate of the component into the development ofa preventive maintenancestrategy,in order to further improve the reliability and maintenance efficiency of dump truck chassis system.Thesimulation results show that the dynamic preventive maintenance strategy considering non-stationary operation of dump truck can effectively improve the vehicle availability and save the maintenance expenses.