Leveraging systems' non-linearity to tackle the scarcity of data in the design of Intelligent Fault Diagnosis Systems

A new method for building vibration-based Intelligent Fault Diagnosis Systems (IFDS) using Deep Transfer Learning (DTL) under severe data scarcity has been proposed by a researcher, with experimental validation performed on a railway pantograph structure [1][2]. The work, submitted to the arXiv preprint repository on 18 June 2026 by Andrea Mattia Garavagno, addresses a core limitation of DTL-based fault diagnosis: the heavy reliance on large volumes of labelled data, which are often difficult to obtain for machine or structural faults [1][2]. The paper introduces a periodic multi-excitation level procedure that exploits the intrinsic non-linearities of real-world systems to generate images. These images are then analyzed by pre-trained Convolutional Neural Networks (CNNs) to identify faults [1][2]. To further combat data scarcity, the author proposes a new data visualization method and an associated augmentation technique [1][2]. The approach was experimentally validated on a railway pantograph structure, providing what the abstract describes as effective support for the method [2]. The preprint, spanning 2,545 KB, was posted at 15:02:18 UTC [1]. arXiv, which began on 14 August 1991, serves as an open-access repository for electronic preprints that are moderated but not peer-reviewed, and it surpassed two million articles by the end of 2021 [6]. The platform also hosts arXivLabs, a framework for community-contributed features that appear as tabs on article pages, though new project proposals are temporarily paused while the development team focuses on migrating systems to the cloud [3][4]. arXivLabs projects, which include tools like the Bibliographic Explorer and CORE Recommender, operate under guidelines that require partners to share arXiv’s values of openness, community, excellence, and user data privacy [4][5].