Unsupervised anomaly detection for conveyor temperature SCADa data

Abstract

Belt conveyor system is a crucial element of ore transport process in underground copper ore mine. Damage of single belt conveyor might cause stopping of huge part of underground transport network, especially when failure concerns the main haulage conveyor line. For that reason it is important to use SCADA monitoring system. The symptom of damage can be found in increasing temperature measured within the system. Unfortunately, operating belt conveyors can be considered as time-varying system and direct decision making using temperature value is difficult. Long-term analysis of time series enables to learn how to recognize alarming moment. Thus the removal of failure can be scheduled so as to minimize the losses in production. In this paper the clustering method was applied to the long-term observations of the temperature in order to gearbox fault detection. Moreover, the breaks in the activity of belt conveyors (no operation) caused by holidays will be determined. The clustering algorithm identifies also the specific character of the work at the beginning and end of week.