A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring
This is an incremental improvement for industrial manufacturing systems, addressing tool wear prediction to enhance maintenance efficiency.
The paper tackles tool condition monitoring by proposing a multi-state diagnosis and prognosis framework using a deep belief network, which improves accuracy and robustness in tool state and wear estimation on a real-world gun drilling dataset.
In this paper, a multi-state diagnosis and prognosis (MDP) framework is proposed for tool condition monitoring via a deep belief network based multi-state approach (DBNMS). For fault diagnosis, a cost-sensitive deep belief network (namely ECS-DBN) is applied to deal with the imbalanced data problem for tool state estimation. An appropriate prognostic degradation model is then applied for tool wear estimation based on the different tool states. The proposed framework has the advantage of automatic feature representation learning and shows better performance in accuracy and robustness. The effectiveness of the proposed DBNMS is validated using a real-world dataset obtained from the gun drilling process. This dataset contains a large amount of measured signals involving different tool geometries under various operating conditions. The DBNMS is examined for both the tool state estimation and tool wear estimation tasks. In the experimental studies, the prediction results are evaluated and compared with popular machine learning approaches, which show the superior performance of the proposed DBNMS approach.