The Intelligent Fault Diagnosis of rotating machinery proposes some
captivating challenges in light of the imminent big data era. Although results
achieved by artificial intelligence and deep learning constantly improve, 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 machine learning diagnosis model, based on intelligent
spectrogram recognition, via image processing. The approach is characterized 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.Comment: 14 pages, 13 figure
