Network platform for automation of pit dump truck failure prediction

The article considers the possibilities of developing an automated system for monitoring and predicting the technical condition of in-pit vehicles at the operation stage based on failure statistics and network analysis of data received from health sensors of the mining machines. This study seeks to reduce emergency downtime in the mining industry by introducing modern information and communication technologies. The applicability of existing methods to analyze digital signals received from the sensors installed on the mining equipment was assessed. A promising approach is considered, using the progress achieved in network engineering and conversion of the time series signals into the integrated networks. A sequence of operations is proposed as an innovation, including collection and analysis of data, development of network prediction models and practical implementation of the results. It is expected that using such a sequence of steps will be able to promptly notify of the need to repair equipment, thereby reducing downtime, which in turn will increase productivity and reduce the operating costs. The main stages of the study are formulated and presented, the implementation of which is aimed at predicting the health of the equipment, identifying the need for unscheduled repairs, which will lead to a decrease in the number of emergency failures or their prevention in real operating conditions of mining enterprises.