A context-aware unsupervised predictive maintenance solution for fleet management

Abstract

We deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.

Artificial Intelligence for Implementation of Predictive Maintenance in Indian Army

“…This includes not only AFVs but also transport trucks, tanks, and support vehicles. The data from these vehicles' sensors and systems is analyzed to predict component failures, leading to optimized maintenance schedules and reduced operational downtime [12]. 4.…”