Randomized eigen-spectrograms extraction for an effective fault diagnosis of bearings

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.