Abstract
Pareto histograms are commonly used to determine maintenance priorities by ranking equipment failure codes according to their relative cost or downtime contribution. However, such histograms do not readily enable identification of the dominant variables influencing downtime and repair costs, namely the failure frequency, mean downtime and mean repair cost associated with each failure code. Advances an alternative method for analysing equipment downtime and repair costs using logarithmic (log) scatterplots. By applying limit values, log scatterplots can be divided into four quadrants enabling failures to be classified according to acute or chronic characteristics and facilitating root cause failure analysis. Log scatterplots permit the identification of frequently occurring failures that consume relatively little repair cost or downtime yet cause frequent operational disturbances leading to production losses. In addition, by graphing the trend of failure data over successive time periods, log scatterplots provide a useful visual means of evaluating the performance of maintenance improvement initiatives. Provides examples of the practical application of log scatterplots by a number of mining companies and mining equipment suppliers in Chile.
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