Anomaly detection in laser-guided vehicles' batteries: a case study

TL;DR

This paper presents a case study on detecting anomalies in the batteries of laser-guided vehicles, highlighting its importance for industrial maintenance and safety within a high-performance computing project.

Contribution

It introduces a novel approach for anomaly detection in LGV batteries within a high-performance computing environment, demonstrating its practical application.

Findings

Effective anomaly detection in LGV batteries was achieved.

The method supports preventive maintenance scheduling.

Enhanced safety and operational efficiency in industrial settings.

Abstract

Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.