Accurate representation of an application duty cycle is dependent on a large number of factors. Due to the amount of variables and complexity and variety of tasks handled by off-highway machinery, it becomes a very difficult to accurately define the 'duty cycle' for a typical application. Based on the wealth of machine development experience within Caterpillar, use of a quick tool was discussed for initial evaluation of LCF (low cycle fatigue). Different categories of machines could be classified under categories of transience and then evaluated for LCF. This method can be further refined against the use of electronic engines on off-highway applications-Limited engine test bed data can be used to easily calculate turbo speeds for some typical duty cycles. The correlation between measured turbo speed and calculated turbo speed was good. This method however, can be further refined by use of algorithms to calculate turbo speed using turbo maps and associated inputs from the engine's ECM. There are numerous factors that affect the resultant accuracy of the calculations. There are various alternative methods, which could be used to determine turbo speed. Amongst the spectrum of models available, there is a trade off of accuracy, speed of execution, speed of construction, speed of validation, expertise and flexibility. A good judgement of the added value of several other accurate methods is essential. There are also a number of deterioration factors on various engine systems that could affect turbo response over its life. Some of the common issues like fuel injector coking or injector growth, increasing pressure drop in the charge-air lines over the course of engine life are some of the factors. Further work will be undertaken in understanding these models to further accommodate some of the engine system related factors effecting LCF. Accommodating the effect of altitude is a key factor to completely understand the risk of LCF on a particular application. Some of the key parameters affecting the engine performance and response at high altitudes are well understood and documented. Therefore adapting the learning to encompass all the different operating conditions for an engine and its effect on turbo speed can be determined.