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

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... 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.