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Sensors (Basel). 2018 Nov 21;18(11). pii: E4069. doi: 10.3390/s18114069.

Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement.

Author information

1
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200245, China. renmj@sjtu.edu.cn.
2
State Key Laboratory of Ultra-Precision Machining Technology, The Hong Kong Polytechnic University, Hong Kong, China. mfbenny@inet.polyu.edu.hk.
3
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200245, China. gaobo.xiao@sjtu.edu.cn.

Abstract

This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.

KEYWORDS:

Gaussian process; Surface measurement; data fusion; multi-sensor measurement; surface modelling

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