Researchers in tool condition monitoring often collect large amount of sensor signal data from experiments to study the complex tool condition relationships with signals. In order to provide new light into this process on a real-time basis, it is critical to identify and detect abnormality at the lowest resolution possible so that the wear behavior on each flute within a tool revolution can be clearly shown. A signal stream clustering method is developed to separate numerous tool-revolution signals into similar groups, each representing a specific set of corresponding events.

Prognostics has the potential to be very valuable in many industries. This is especially the case in the petroleum industry where the costs of tool failure are extremely high and continue to increase. Previous attempts have been made to predict the remaining useful life of drilling tools. While the developed methods were shown to be able to accurately predict the remaining useful life, the data requirement was such that they had limited or no viability in "real world" operations.