A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
A probabilistic framework for online structural health monitoring: active learning from machining data streams
Journal of Physics, Conference Series
A critical issue for data-based engineering is a lack of descriptive labels for the measured data. For many engineering systems, these labels are costly and/or impractical to obtain, and as a result, conventional supervised learning is not feasible. This paper suggests a probabilistic framework for the investigation and labelling of engineering datasets; specifically, acoustic emission data streams recorded online from a turning machine. Two alternative probabilistic measures are suggested todoi:10.1088/1742-6596/1264/1/012028 fatcat:xlcmyz4fovbkxmthw23uzm4aeq