Binghai Zhou, Jiadi Yu, Jianyi Shao and Damien Trentesaux
The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect effect into…
Abstract
Purpose
The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect effect into maintenance activities.
Design/methodology/approach
On the analysis of availability and maintenance cost, preventive maintenance (PM) models subjected to imperfect maintenance for different equipment types are built. And then, a cost-saving function of OM is established to find out an optimal OM strategy, depending on whether the front-bottleneck machines adopt OM strategy or not. A numerical example is given to show how the proposed bottleneck-based OM model proceeded.
Findings
The simulation results indicate that the proposed model is better than the methods to maintain the machines separately and the policy to maintain all machines when bottleneck machine reaches its reliability threshold. Furthermore, the relationship between OM strategy and corresponding parameters is identified through sensitivity analysis.
Practical implications
In practical situations, the bottleneck machine always determines the throughput of the whole series production system. Whenever a PM activity is carried out on the bottleneck machine, there will be an opportunity to maintenance other machines. Under such circumstances, findings of this paper can be utilized for the determination of optimal OM policy with the objective of minimizing total maintenance cost of the system.
Originality/value
This paper presents a bottleneck-based OM optimization model with the integration of the imperfect effect as a new method to schedule maintenance activities for a series production system with buffers. Furthermore, to the best of the knowledge, this paper presents the first attempt to considering the bottleneck constraint on system capacity and diverse types of machines as a means to minimize the maintenance cost and ensure the system throughput.
Details
Keywords
Jiadi Qu, Fuhai Zhang, Yili Fu, Guozhi Li and Shuxiang Guo
The purpose of this paper is to develop a vision-based dual-arm cyclic motion method, focusing on solving the problems of an uncertain grasp position of the object and the…
Abstract
Purpose
The purpose of this paper is to develop a vision-based dual-arm cyclic motion method, focusing on solving the problems of an uncertain grasp position of the object and the dual-arm joint-angle-drift phenomenon.
Design/methodology/approach
A novel cascade control structure is proposed which associates an adaptive neural network with kinematics redundancy optimization. A radial basis function (RBF) neural network in conjunction with a conventional proportional–integral (PI) controller is applied to compensate for the uncertainty of the image Jacobian matrix which includes the estimated grasp position. To avoid the joint-angle-drift phenomenon, a dual neural network (DNN) solver in conjunction with a PI controller and dual-arm-coordinated constraints is applied to optimize the closed-chain kinematics redundancy.
Findings
The proposed method was implemented on an industrial robotic MOTOMAN with two 7-degrees of freedom robotic arms. Two experiments of carrying a tray repeatedly and turning a steering wheel were carried out, and the results indicate that the closed-trajectories tracking is achieved successfully both in the image plane and the joint spaces with the uncertain grasp position, which validates the accuracy and realizability of the proposed PI-RBF-DNN control strategy.
Originality/value
The adaptive neural network visual servoing method is applied to the dual-arm cyclic motion with the uncertain grasp position of the object. The proposed method enhances the environmental adaptability of a dual-arm robot in a practical manipulation task.