Fault diagnosis of injection molding machine non-return valve based on data-driven model

Non-return valve (NRV) is one of the key components in determining the consistency of the quality of the products molded by injection molding machine. Wear on the NRV affects the quality of the molded product. Nevertheless, detecting wear on the NRV can be challenging and disassembly of the machine is the only diagnostic method, which can have a negative impact on productivity. In this paper, a data-driven fault diagnosis method is proposed, which uses Stacked Auto Encoder (SAE) to analyze the pressure, torque, and displacement signals of the injection molding machine and combined with XGBoost (Extreme Gradient Boosting) to diagnose the faults of the NRV. The experimental results indicate that the SAE-XGBoost method accurately predicts NRV failures. Compared to using only XGBoost for prediction, the accuracy has improved from 97.5% to 99.6%. Eventually, the SAE-XGBoost model is integrated into the control program of the injection molding machine in the form of functional modules. Throughout the production process, the model adeptly monitors and identifies the production profile, promptly dispatching warning messages to users when diagnosing NRV wear. This facilitates intelligent diagnosis of the service status of injection molding machine components, which will have a positive influence on improving the production efficiency and intelligence of injection molding machines. The results of this study represent a synergistic application of artificial intelligence and time-domain statistical features in the realm of fault diagnosis for injection molding machines. This has the potential to significantly broaden the scope of AI utilization within the domain of injection molding processes, thereby advancing the intelligent technology associated with injection molding machines.