  The Intelligent Fault Diagnosis of rotating machinery proposes some
captivating challenges in light of the imminent big data era. Large amounts of
data are expected to populate the Internet of Things (IoT) diagnostic services.
Consequently, todays deep learning strategies are evolving towards effective
approaches such as transfer learning to uncover hidden paths in extensive
vibration data. However, this field is characterized by several open issues.
Models interpretation is still buried under the foundations of data driven
science, thus requiring attention to the development of new opportunities also
for machine learning theories. This study proposes a diagnosis model, based on
intelligent spectrogram recognition, via image processing. The novel approach
is embodied by the introduction of the eigen-spectrograms and randomized linear
algebra in fault diagnosis. The eigen-spectrograms hierarchically display
inherent structures underlying spectrogram images. Also, different combinations
of eigen-spectrograms are expected to describe multiple machine health states.
Randomized algebra and eigen-spectrograms enable the construction of a
significant feature space, which nonetheless emerges as a viable device to
explore models interpretations. The computational efficiency of randomized
approaches further collocates this methodology in the big data perspective and
provides new reading keys of well-established statistical learning theories,
such as the Support Vector Machine (SVM). The conjunction of randomized algebra
and Support Vector Machine for spectrogram recognition shows to be extremely
accurate and efficient as compared to state of the art results and transfer
learning strategies.
