  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.
