The aim of this study is to apply computed order tracking with subsequent rotation domain averaging and statistical analysis to typical mining environments. Computed order tracking is a fault detection method that is unaffected by varying speed conditions often found in industry and has been proven effective in laboratory conditions. However in the controlled environment of a laboratory it is difficult to test the robustness of the order-tracking procedure. The need thus exists to adjust the order tracking procedure so that it will be effective in the mining environment. The procedure needs to be adjusted to function with a two pulse per revolution speed input. The drag gear aboard a dragline rotates in two directions. This gives the unique opportunity to observe the performance of the order tracking method in a bi-directional rotating environment allowing relationships between the results of each operating direction to be investigated. A monitoring station was set up aboard a dragline and data was captured twice daily for a period spanning one year. The data captured consisted of accelerometer and proximity sensor data. The key on the shaft triggers the proximity sensors allowing speed and direction to be measured. The rudimentary measured speed is interpolated using various documented speed interpolation techniques and by a newly developed speed interpolation technique. The interpolated speed is then used to complete the order tracking procedure that re-samples the vibration data with reference to the speed. The results indicate that computed order tracking can be successfully implemented in typical mining environments. Furthermore there is a distinct relationship between vibration data taken in both rotational directions: one direction provides a better indication of incipient failure. It is thus important not to choose a direction randomly when monitoring rotating machinery of this kind.
Dissertation (MEng)--University of Pretoria, 2008.