A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring

TL;DR

This paper introduces a multi-state diagnosis and prognosis framework using deep belief networks for tool condition monitoring, capable of automatic feature learning and handling imbalanced data, validated on real-world drilling data.

Contribution

It presents a novel multi-state framework with a cost-sensitive deep belief network for improved tool condition diagnosis and prognosis, addressing data imbalance and feature learning.

Findings

Superior accuracy and robustness over traditional methods

Effective handling of imbalanced data in tool diagnosis

Validated on real-world drilling dataset

Abstract

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.