A Measurement Frequency Estimation Method for Failure Prognosis of an Automated Tire Condition Monitoring System

The ongoing digitalization allows operators and manufacturers to constantly gain new insights about their asset’s performance and degradation status. This information could potentially help to reduce operating and maintenance costs. Although significant amount of research has been spent in determining Remaining Useful Lifetimes (RUL) of various systems, these efforts often implicitly assume an unrestricted availability of measurement data. However, the amount of acquired data significantly drives the necessary investment cost or is sometimes even impossible to obtain in required requencies in reality. In this paper, we will investigate the changes of the precision for the RUL prognosis on the example of a Tire Pressure Indication System (TPIS). After a possible layout with sensor requirements for a fully automated condition monitoring system has been developed in theory, we describe necessary data cleansing steps to account for environmental impacts on the system’s performance and to derive the system’s health status. With the help of a Monte Carlo (MC) simulation, we evaluate the system’s sensitivity towards changes in precision of the RUL for different measurement frequencies, prognostic models, and parameter settings. The results allow an estimation of the minimum pressure measurement frequency for a fully automated TPIS in order to obtain the required prognostic performance and to maximize cost efficiency

Evaluation of Prescriptive Maintenance Strategies for a Tire Pressure Indication System (TPIS) assuming Imperfect Maintenance

Digital technologies for condition monitoring enable failure predictions of aircraft systems and will have an enormous impact on aircraft maintenance in the future. Current research studies evaluate the potential of these technologies within prescriptive maintenance strategies. These strategies combine failure predictions of a system with given operational environmental conditions to identify the optimal time for maintenance. However, current prescriptive maintenance models neglect the impact of imperfect maintenance, which affects system reliability, system availability and its associated costs. The goal of this thesis is to investigate how this impact of imperfect maintenance affects prescriptive maintenance strategies including the tire pressure indication system. In addition, an aviation maintenance stakeholder model for a prescriptive simulation model of the tire pressure indication system is presented, which will allow for more precise strategy development in future implementations. This aviation maintenance stakeholder model includes the stakeholders aircraft maintenance, airline, mechanics, logistics service providers, and component maintenance. The imperfect maintenance model considers the impact of human factors in addition to technological influences. The effects of fatigue, procedure design, certifications, training, experience, age, and environmental conditions are included into the model to determine the degree of imperfection of a performed task. Imperfect maintenance considers discretely performed maintenance tasks and calculates a new system state after the execution of an imperfect repair. For the development of prescriptive maintenance strategies for the tire pressure indication system, the obtained results of a sensitivity analysis are used. In this context, the parameters of the imperfect maintenance model are analyzed with regard to the objective maintenance cost. These strategies are subsequently compared to the defined state-of-the-art tire maintenance process. In comparison, the application of the tire pressure indication system in addition to human factor optimization indicates the highest cost savings potential of 67 percent