Tensor Based Algorithm for Automatic Partial Discharges Pattern Classification

The development of automated tools capable of monitoring electric motors is important for industrial applications. The measure of partial discharges is one of the most prominent methodologies for the evaluation of electric motors operating conditions. This work proposes to apply tensor-based classification methods to discriminate between different partial discharges sources. The major advantage of tensor-based approaches relies on the fact that they can inherently handle partial discharges pattern using all the geometrical information embedded in the patterns. Secondly, noise filtering can be embedded in classification mechanism, without manual tuning. As a consequence, better performances are obtained in presence of additive sources of noise like 4G, GSM connections and electromagnetic sources that could be detected by conducted and irradiated sensors especially during an online monitoring. To demonstrate the ability of the tensor-based methods to overcome the noise presence, tests involved defects with different levels of addictive noises. Experimental results proved the effectiveness of the proposed approach.