Technical condition change detection using Anderson–Darling statistic approach for LHD machines – engine overheating problem

Abstract

In underground mine so called Load-Haul-Dump machines (LHD) plays a key role in horizontal transportation process. LHD machines execute ore haulage from mining faces to dumping points in a cyclic way. Time-varying and harsh environmental conditions determine high workload, so effectiveness demands are big challenges for maintenance staff. One of the most important issue is related to engine overheating, what is the main cause of unjustifiable loader stoppages and unwanted disturbances in production. Operator is obligated to react quickly and switch machine to idle operation until it cools down. Existing on-board monitoring systems dedicated for LHD machines provide data necessary to perform diagnostics of the engine as well as its cooling system. Understanding how load, wear level of machine and ambient temperature influence diagnostic data is the key in development of fault detection algorithms. In this paper, authors propose to use longterm temperature data. The Anderson–Darling statistic is applied in order to detect segments of different statistical properties which are related to different technical condition. Analysis of obtained two-dimensional data structure allows to find points of change of technical condition of the machine. It could be considered as training for diagnostic system that could be used for machine monitoring.

Keywords:

• Load-Haul-Dump machines
• Anderson–Darling statistic
• condition monitoring
• statistical analysis

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Framework Programme for Research and Innovation Horizon 2020 [grant agreement number 636834] (DISIRE – Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock).