Automatic calculation of thresholds for load dependent condition indicators by modelling of probability distribution functions – maintenance of gearboxes used in mining conveying system

Abstract

Limit values for gearbox vibration-based condition indicators are key to determine in order to be able to estimate moment when object is in a need of maintenance. Further decision making process usually might utilize simple if-then-else rule using established threshold values. If diagnostic data takes the values from the Gaussian distribution, finding the decision boundaries is not difficult. Simplistically, that comes down to standard pattern recognition technique for “good condition” and “bad condition” based on probability density functions (PDFs) of diagnostic data. This situation is becoming more and more complicated when distribution is not Gaussian. Such cases require to develop much more advanced analytically solution. In this paper, we present the case of belt conveyor’s gearbox for which PDFs of diagnostic features overlap each other because of strong influence of time varying operating conditions on spectral features. New approach to automatic threshold recognition has been proposed based on modeling diagnostic features with Weibull distribution and using agglomerative clustering to distinguish classes of technical condition, which leads to determination of thresholds separating them.