A probabilistic framework for online structural health monitoring: active learning from machining data streams

L A Bull, K Worden, T J Rogers, C Wickramarachchi, E J Cross, T McLeay, W Leahy, N Dervilis
2019 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 to
more » ... lect the most informative observations. During machining operations, these data would then be investigated and annotated by an engineer, in order to maximise the classification performance of a statistical model used to predict tool wear.
doi:10.1088/1742-6596/1264/1/012028 fatcat:xlcmyz4fovbkxmthw23uzm4aeq