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Practical research in AI often lacks of available and reliable datasets so the practitioners can try different algorithms. The field of predictive maintenance is particularly challenging in this aspect as many researchers don't have access to full-size industrial equipment or there is not available datasets representing a rich information content in different evolutions of faults. In this paper, it is presented a dataset with evolution of typical faults (commutator, winding and brush wear) in inexpensive DC motors under extensive monitoring (vibration, temperature, voltage, current and noise). These motors exhibit a particularly short useful life when operating out of nominal conditions (from 30 minutes to 6 hours) which make them very interesting to test different signal processing algorithms and introduce students and researchers into signal processing, fault detection and predictive maintenance. The paper explains in detail the experimentation and the structure of the real, un-processed, dataset published in the AI4EU platform with the aim of complying with the FAIR principle so the dataset is Findable, Accessible, Interoperable and Reusable.

F.A.I.R. open dataset of brushed DC motor faults for testing of AI algorithms