To evaluate the impact of major overhaul inspections on helicopter system reliability and maintainability by conducting an analysis using the Non-Homogeneous Poisson Process (NHPP) and Crow-AMSAA models to improve operational safety and reliability during scheduled overhauls.
A robust analytical framework is employed for analyzing helicopter key subsystems data (Airframe, Engine, Electrical, Instrument and Radio Systems) undergoing their second major overhaul. The study utilizes the Crow-AMSAA model as the specific “power-law” version of the NHPP to derive mathematically consistent parameters and perform trend testing. This approach is more appropriate for assessing helicopter repairable systems, which are restored to service after major inspections rather than replaced.
The NHPP and Crow-AMSAA analysis, appropriately handling helicopter reliability and accurately addressing repairable systems, recognized consistent reliability degradation (all β > 1). This indicates that while the major overhauls temporarily restored functionality, they did not decrease the failure rate. Benchmarking against international standards showed lower reliability and higher failure rates than those of global norms. Airframe systems proved to be the most reliable, whereas electrical and radio subsystems displayed notable degradation.
This study is based on empirical failure data obtained from a specific helicopter fleet operating within a defined maintenance and operational context. Although the analytical framework and modelling approach are robust and theoretically sound, the findings may reflect conditions specific to the studied system.
Choosing suitable reliability models is essential for assessing helicopter repairable systems. The application of NHPP and Crow-AMSAA derived hazard rates facilitates a considerable decrease in life-cycle operational costs and a marked improvement in fleet operational safety and readiness.
The study has positive social implications for passenger safety, crew protection and mission effectiveness. By supporting more informed maintenance and overhaul decisions.
This study presents a novel analytical framework that corrects the commonly misapplied use of nonrepairable methods by introducing a segmented Crow–AMSAA analysis with formal trend tests to better assess major overhaul effectiveness.
