Predictive Maintenance and Fault Detection in Open-Pit Mining Shovels: A Systematic Literature Review

Abstract

Predictive maintenance has become a key strategy for improving reliability and availability in large-scale open-pit mining operations, where mining shovels play a critical role in the loading process. This paper presents a Systematic Literature Review focused on predictive maintenance, condition monitoring, and fault detection applied to mining shovels. Following the PRISMA guidelines and the PICOC framework, 48 studies indexed in Scopus were analyzed. The results show a growing adoption of data-driven approaches, particularly machine learning and signal processing, mainly based on vibration data and focused on structural subsystems. However, challenges related to industrial validation, data quality, generalization, and interpretability remain.