Multiscale Shared Learning for Fault Diagnosis of Rotating Machinery in Transportation Infrastructures

Rotating machinery is ubiquitous, and its failures constitute a major cause of the failures of transportation infrastructures. Most fault-diagnosis methods for rotating machinery are based on vibration-signal analysis because vibrations directly reflect the transient regime of machinery elements. This article proposes a novel multiscale shared-learning network (MSSLN) architecture to extract and classify the fault features inherent to multiscale factors of vibration signals. The architecture fuses layer-wise activations with multiscale flows, to enable the network to fully learn the shared representation with consistency across multiscale factors. This characteristic helps MSSLN provide more faithful diagnoses than existing single- and multiscale methods. Experiments on bearing and gearbox datasets are used to evaluate the fault-diagnosis performance of transportation infrastructures. Extensive experimental results and comprehensive analyses demonstrate the superiority of the proposed MSSLN in fault diagnosis for bearings and gearboxes, the two foundational elements in transportation infrastructures.