statistical tool = Weibull distribution

Preventive maintenance (PM) schedules often use the Weibull distribution because it's a versatile statistical tool capable of modeling different failure rates (increasing, constant, or decreasing) over an equipment's life, helping to optimize maintenance by predicting when failures are likely to occur.

https://www.google.com/search?q=Preventive+maintenance+schedules+are+often+based+on+the+Weibull+distribution

because

https://www.google.com/search?q=Weibull+distribution+is+useful+for+failure+rate+modeling

Hopefully, this might help somewhat.

Planning aircraft maintenance is essential for enhancing its performance. This involves assessing failure data so that patterns can be detected and the probability of a component failing determined. In this respect, several statistical methods are utilized, including reliability modeling, survival analysis, and predictive analytics. These methods assist in the quantification of failure rates and the length of time between various failures. Planning can be undertaken to ensure the aircraft fails at a planned time by assessing varying operational conditions. Planners can create data-driven, dynamic, and successful schedules by utilizing techniques such as machine learning algorithms, regression analysis, and Bayesian Inference, which are geared toward reducing downtime and increasing the reliability of the aircraft system. This is crucial to ensuring airplane assets are well-utilized and the aircraft system largely reliable. Planning for routine aircraft support is pivotal in boosting its functionality. This is feasible through the evaluation and conservation of failure information to identify and predict component failure probabilities by implementing statistical methodologies like reliability modeling, survival analysis, predictive analytics, regression analysis, as well as some machine learning algorithms. The above methods are readily applied to establish failure time intervals and failure rates. The maintenance team proactively strategizes operationally using diverse situations to make informed decisions. With statistical analysis and computerized systems, such as regression modeling, machine learning, and software, planners develop schedules that dynamically change and proactively prevent functional failure. Comprehensive planning guarantees that the aircraft asset's utilization is optimized, and the overall flying system becomes significantly more dependable.