Automated Hyper-parameter Tuning for Machine Learning Models in Machine Health Prognostics

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Published Sep 24, 2018
Wang-Chi Cheung Weiwen Zhang Yong Liu Feng Yang Rick-Siow-Mong Goh

Abstract

Recent studies have revealed the success of data-driven machine health monitoring, which motivates the use of machine learning models in machine health prognostic tasks. While the machine learning approach to health monitoring is gaining importance, the construction of machine learning models is often impeded by the difficulty in choosing the underlying hyper-parameter configuration (HP-config), which governs the construction of the machine learning model. While an effective choice of HP-config can be achieved with human effort, such an effort is often time consuming and requires domain knowledge. In this paper, we consider the use of Bayesian optimization algorithms, which automate an effective choice of HP-config by solving the associated hyperparameter optimization problem. Numerical experiments on the data from PHM 2016 Data Challenge demonstrate the salience of the proposed automatic framework, and exhibit improvement over default HP-configs in standard machine learning packages or chosen by a human agent.

How to Cite

Cheung, W.-C., Zhang, W., Liu, Y., Yang, F., & Goh, R.-S.-M. (2018). Automated Hyper-parameter Tuning for Machine Learning Models in Machine Health Prognostics. Annual Conference of the PHM Society, 10(1). https://doi.org/10.36001/phmconf.2018.v10i1.490
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Keywords

Machine Learning, Data-driven prognostic, automation

Section
Technical Papers