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A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring

A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring

Omid Geramifard, Jian-Xin Xu, Junhong Zhou
Copyright: © 2013 |Pages: 24
ISBN13: 9781466620957|ISBN10: 1466620951|EISBN13: 9781466620964
DOI: 10.4018/978-1-4666-2095-7.ch011
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MLA

Geramifard, Omid, et al. "A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring." Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, edited by Seifedine Kadry, IGI Global, 2013, pp. 205-228. https://doi.org/10.4018/978-1-4666-2095-7.ch011

APA

Geramifard, O., Xu, J., & Zhou, J. (2013). A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring. In S. Kadry (Ed.), Diagnostics and Prognostics of Engineering Systems: Methods and Techniques (pp. 205-228). IGI Global. https://doi.org/10.4018/978-1-4666-2095-7.ch011

Chicago

Geramifard, Omid, Jian-Xin Xu, and Junhong Zhou. "A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring." In Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, edited by Seifedine Kadry, 205-228. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2095-7.ch011

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Abstract

In this chapter, a temporal probabilistic approach based on hidden semi-Markov model is proposed for continuous (real-valued) tool condition monitoring in machinery systems. As an illustrative example, tool wear prediction in CNC-milling machine is conducted using the proposed approach. Results indicate that the additional flexibility provided in the new approach compared to the existing hidden Markov model-based approach improves the performance. 482 features are extracted from 7 signals (three force signals, three vibration signals and acoustic emission) that are acquired for each experiment. After the feature extraction phase, Fisher’s discriminant ratio is applied to find the most discriminant features to construct the prediction model. The prediction results are provided for three different cases, i.e. cross-validation, diagnostics, and prognostics. The possibility of incorporating an asymmetric loss function in the proposed approach in order to reflect and consider the cost differences between an under- and over-estimation in tool condition monitoring is also explored and the simulation results are provided.

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