Model-Based Condition-Based Maintenance of Mining Shovels using Electric Motor Signal Information

Abstract

A study to assess the condition of a mining shovel is presented. Signal information generated by electric motors is used in addition to more conventional data such as provided by accelerometers and strain gages. In particular, the case of the hoist system of the shovel is described in which the motor operates at variable speed throughout the work cycle. Using motor data, events such as backtorque and gear tooth backlash related impacts can be detected and monitored. The repetitive nature of the shovel motions along with the automatic control behavior generate patterns that can help identify the instantaneous motion and significant events within the cycle. In certain cases, such as that presented here, information given by electric motor signals are better or easier for predictive maintenance than pure vibration analysis. Nevertheless, both types of signals can be used to complement each other in a more general situation.