Adaptive robust fault detection and control for injection machine mold closing process with accurate parameter estimations

Injection machine is a kind of manufacturing equipment widely used in plastic industry. The mold closing is one of the key actions of injection machine, which is driven by servo motor through gear and ball-screw structure. The main challenges of the mold closing is to achieve a good tracking performance and ensure the mold safety in the meanwhile. In the most of exist controllers, a traditional controller is adopted to track the trajectory, which ignores the parameter uncertainties and disturbances in controller design. And a history-based fault detection is used, which identify unwilling forces acting on the mold(such as products or workers are get stuck) by simply comparing the control inputs with the restored reference value. Since the actual external force is not identified, this kind of fault detection is easily effected from the parameter various and may lead to unwilling actions (e.g. stop the machine frequently). In this paper, we propose adaptive robust fault detection and control for injection machine with accurate parameter estimations including the model parameters (such as mass, frictions) and disturbances(such as unexpected external forces), and the tracking performance and fault detection ability are improved simultaneously. Comparative simulations are carried out and the results show the better performance of our proposed scheme.