Optimisation of mining machinery maintenance in modern mining enterprises through text mining and machine learning techniques

The efficiency of planning processes is fundamental to the success of modern mining enterprises,
particularly in maintaining competitiveness in the raw materials market. This paper addresses the
critical challenge of optimising production lines, encompassing strategic arrangement of mining
fronts and the management of drilling, transport, and auxiliary machinery. Given the dynamic and
often challenging conditions of underground mining, mitigating random disruptions that can lead to
machine downtime is crucial for sustaining productivity. This study highlights the importance of
leveraging large volumes of diverse data to achieve situational awareness and facilitate informed
decision-making under uncertainty. We explore how text mining and machine learning techniques
can extract valuable insights from unstructured data, such as equipment failure reports, which often
contain complex and ambiguous information. By developing a classification system that categorises
failure descriptions into actionable insights, we aim to improve data interpretation and support
predictive maintenance strategies. Furthermore, we propose an integrated approach that
consolidates data from various sources, including downtime logs and repair records, to establish a
comprehensive database for analysis. The proposed methods not only streamline data management
but also enhance the accuracy of predictive models, ultimately enabling mining companies to
optimise their operations and increase overall productivity.