Distributed AI for Special-Purpose Vehicles

Abstract

In this paper, we elaborate on two issues that are crucial to consider when exploiting data across a fleet of industrial assets deployed in the field: 1) reliable storage and efficient communication of large quantities of data in the absence of continuous connectivity, and 2) the traditional centralized data analytics model which is challenged by the inherently distributed context when considering a fleet of distributed assets. We illustrate how advanced machine learning techniques can run locally at the edge, in the context of two industry-relevant use cases related to special-purpose vehicles: data compression and vehicle overload detection. These techniques exploit real-world usage data captured in the field using the I-HUMS platform provided by our industrial partner ILIAS solutions Inc.