Research On Fault Characterization And Reliability Index Prediction And Allocation Of Industrial Robot

With the need of industrial transformation and upgrading in China and the rise of labor costs,the market share of industrial robots is gradually increasing in China,and industrial robots are widely used in welding,assembling,stacking and handling,etc.At present,the control system and key components of industrial robots mainly rely on imports due to a large gap of the reliability between domestic industrial robots and the international advanced level.The low reliability of industrial robot has become a key factor that seriously restricts the development of domestic medium and high grade industrial robot.It is urgent to improve the reliability of domestic industrial robot.In this paper,the fault mode characterization,reliability prediction and reliability allocation of domestic industrial robots are studied.Firstly,the structure and function of each subsystem for six-axis industrial robot is introduced,and the reliability mathematical model is constructed.The FMECA(Failure Mode Effect and Criticality Analysis)was used to analyze the failure mode,failure causes,influencing factors and improvement measures of industrial robots,the qualitative hazard matrix was used to analyze the fault criticality,and finally the improvement suggestions were given.Next,the reliability parameters of industrial robots are determined to lay the foundation for the prediction and allocation of industrial robots.The Failure rate is expected to have guiding significance for maintenance strategy and spare parts quantity of the industrial robot.The fault probability distribution of each subsystem for the industrial robot is different,the fault data cannot be screened and the influence factors of failure rate are complex.Based on the above disadvantage,the failure rate model of industrial robot subsystem is established in different distribution,the fault data as input/output of fitting bathtub curve using neural network in the FMECA,meanwhile,the failure data are filtered via gradient value,to get the bathtub curve of early fault point.The maximum likelihood function is utilized to fitted different distribution parameters and the interval analytic hierarchy process(AHP)was used to refine the factors,which influence the failure rate prediction of industrial robots and quantify the epistemic uncertainty.Then the failure rate correction coefficient was solved by combining the differences between the new industrial robots and the original industrial robots to obtain the failure rate function and the mean time between failure(MTBF)of the new industrial robots.Finally,the reliability allocation of the industrial robots is carried out,which is one of the important means to improve their full cycle life and weak subsystems,and reduce maintenance cost.A reliability allocation method called Multistate Industrial Robot System-Reliability Allocation Method-based Epistemic Uncertainty(MIRS-RAM-EU)is proposed based on the strong customization of complex system and less sample data as well as unclear degradation and failure mechanism,which generates the epistemic uncertainty for industrial robots.The Dempster-Shafer(D-S)evidence theory was used to quantify the epistemic uncertainty,the FMECA data was used to obtain the transfer intensity,the Kolmogorov differential equation was used to calculate the performance of its multi-state subsystem,and the Birnbaum importance theory was used to assign the reliability of the multi-state industrial robot system.Then the reliability allocation coefficient of each industrial robot subsystem at each moment under the epistemic uncertainty is obtained.Finally,this method is compared with the traditional importance method to manifest its accuracy and effectiveness.