Affiliation:
1. The School of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130103, China
2. The School of Mechanical and Electrical Engineering, Changchun Institute of Technology, Changchun 130103, China
Abstract
It is extremely important to monitor the health status of welding robots for the safe and stable operation of a body-in-white (BIW) welding production line. In the actual production process, the robot degradation rate is slow and the effective data are poor, which can reflect a degradation state in the large amount of obtained monitoring data, which causes difficulties in health status evaluation. In order to realize the accurate evaluation of the health status of welding robots, this paper proposes a health status evaluation method based on the evidential reasoning (ER) rule, which reflects the health status of welding robots by using the running state data monitored in actual engineering and through the qualitative knowledge of experts, which makes up for the lack of effective data. In the ER rule evaluation model, the covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm is used to optimize the initial parameters of the evaluation model, which improved the accuracy of health status evaluations. Finally, a BIW welding robot was taken as an example for verification. The results show that the proposed model is able to accurately estimate the health status of the welding robot by using the monitored degradation data.
Funder
Jilin Provincial Science and Technology Development Project
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference24 articles.
1. HMM-TCN-based health assessment and state prediction for robot mechanical axis;Xiao;Int. J. Intell. Syst.,2021
2. A data-driven approach to diagnostics of repetitive processes in the distribution domain—Applications to gearbox diagnostics in industrial robots and rotating machines;Bittencourt;Mechatronics,2015
3. Fault diagnosis and location of the aero-engine hydraulic pipeline based on Kalman filter;Li;Adv. Mech. Eng.,2017
4. Li, C.Y., Chen, H., Xiahou, T.F., Zhang, Q., and Liu, Y. (2022). Health status assessment of radar systems at aerospace launch sites by fuzzy analytic hierarchy process. Qual. Reliab. Eng. Int., 1–15.
5. Song, G.X., and Wang, Z.J. (2023). An Efficient Hidden Markov Model with Periodic Recurrent Neural Network Observer for Music Beat Tracking. Electronics, 11.