MAINTAINABILITY ANALYSIS OF THE SPECIAL PURPOSE VEHICLE ENGINE

Abstract

In the realm of technical systems, the foundation of uninterrupted performance and cost-effective maintenance lies in reliability and maintainability. These pillars ensure that systems function flawlessly and that the processes of maintenance are as efficient as possible. This paper provides an insight into the complex world of maintenance time distributions, with a singular goal: to optimize maintenance strategies and trim downtime's impact. Specifically, our study methodically examines three well-known probability distribution models-Rayleigh, Weibull, and exponential distributions-for their efficacy in capturing the patterns of maintenance times, all within the context of special-purpose vehicle engines. This choice of focus is not arbitrary, as these engines operate in high-stakes environments where reliability and efficient maintenance are vital. To fortify our investigation, we draw from a rich dataset derived from the analysis of 97 documented instances of failures within this specialized engine domain. This dataset forms the empirical backbone of our study. Within this dataset, we conduct a comprehensive analysis, calculating and assessing a spectrum of essential metrics. These include maintainability functions, cumulative distribution function, and probability density functions of maintenance times. These analytics gives us a better overview of the maintenance process itself and all its complex aspects. Furthermore, our research rigorously applies these distribution models to our empirical data, subjecting them to a meticulous assessment of their appropriateness. This rigorous evaluation leverages statistical tests-namely, the Kolmogorov-Smirnov and Pearson's χ2 tests. Our findings reveal that all three distribution models perform commendably in approximating maintenance time data, effectively meeting the rigorous criteria set by these statistical tests. However, when we delve deeper into the analysis, the Rayleigh distribution emerges as the prime candidate, particularly for maintenance times exceeding four hours. This preference can be attributed to the Rayleigh distribution's unique characteristics, which closely align with the underlying patterns inherent in our empirical data. The implications of our research extend far beyond this specific context. Our insights are transferrable to the broader landscape of modeling maintenance time distributions, offering actionable guidance for real-world scenarios. Our unequivocal recommendation of the Rayleigh distribution for cases involving extended maintenance times can significantly influence decision-makers, elevating their maintenance strategies and processes to new heights. Ultimately, our research serves as a catalyst for enhancing the reliability and efficiency of technical systems, ensuring their sustained performance in the face of evolving challenges.