Alternative method for equipment condition monitoring on South African mines

Abstract

The practicality of accurate condition monitoring and fault diagnostics depends on the type of parameter measured and the accuracy of the measurement. In the South African mining industry, it is common to find large electrical machines with limited logged parameters, which significantly decreases fault diagnostic capability. In this study, a condition monitoring methodology that incorporates an autoregressive fault detection model is developed to improve condition-based maintenance strategies on South African mines. Autoregressive models have shown to be able to detect and predict equipment defects with available temperature parameters. A method to determine the condition of equipment is developed by establishing an autoregressive model on the modal parameters of both healthy and unhealthy machines. The method was validated by comparing results with the mine's maintenance reports. The model was implemented in two case studies which include large three-phase induction motors. Case Study 1 presents a large disturbance in the temperature of a non-drive end bearing of a multistage centrifugal compressor that was detected by the model. Case Study 2 presents a gradually increasing motor winding temperature of a multistage centrifugal pump that was also successfully detected. The method is a viable alternative to the mines due to the capability of automatically detecting faults even within the mines' alarm and trip limits. The model automatically adapts to the behaviour of the input parameters and monitors the mean and variance shifts. This allows the method to be interchangeable with different types of equipment. The method can continuously evaluate a system of multiple components and provide simple, actionable feedback if a fault is detected.