**5. Conclusions**

We have presented here an innovative methodology and an associated fast and efficient software system prototype, for the algorithmic prediction of industrial machine tools malfunctions, adaptable to any type of company. It integrates

machinery and physical devices with the analytical technologies of Text Mining and allows the identification of anomalous behavior of a machine tool, even of minimal entity, rarely perceived by other strategies.

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The system performs its analysis without waiting for the end of the shift or a machine stop. After recognition, it can initiate automatic safeguard procedures, call a human expert, or schedule some minor tuning operations. The system works without waiting for the shift to end or the machine to stop.

The algorithms require linear execution time on the number of input characters, run on a data structure completely on RAM and are independent on the data structure size, which can be modified without actual computational costs, as it is not sorted. A classic approach, on the other hand, requires searching for a message in a set of possible messages using efficient algorithms, which work largely on secondary memories and depend on the size of the data structure that need to be, necessary, sorted, and therefore it takes time, not irrelevant, to add, modify or delete an entry in it, possibly by physically moving items from one memory area to another.

Last, but not least, is the fact that a classic approach is inadequate because a log message is made up of many words and others non-alphabetic symbols and the data structure size could be very large.

We believe that this approach can bring significant competitive advantages to a company in which the effective and precise predictive analysis of machine tools is a necessity to be pursued by spending as little time as possible, obtaining as precise a result as possible, limiting false recognition errors, as much as possible.
