Proposing a framework for reliability estimation using a proportional hazards model based on diesel engine condition monitoring data
This study aims to improve diesel engine reliability estimation by using a risk-based model that incorporates key environmental factors, especially wear particles in engine oil, for more accurate analysis than traditional time-based methods.
The Proportional Hazards Model (PHM) was used to assess engine reliability based on wear particles in oil. The Harrell and Lee test checked model assumptions, and the Wald test validated coefficients. Reliability was then compared across two engine groups under different conditions.
The study's results showed that incorporating risk factors, such as the level of wear particles in engine oil, increases the accuracy of reliability estimation for diesel engines. Specifically, it was found that engine age, maintenance status, and operational conditions significantly impact reliability, such that worn-out engines reach lower levels of reliability more quickly. The proposed model, by providing a more precise analysis, can serve as an effective tool for optimizing maintenance scheduling and preventing unexpected failures in industrial systems.
Originality/Value:
This research's primary distinction lies in the integration of qualitative data related to the internal condition of the engine (wear particles in oil) with advanced statistical models of PHM, which has been less addressed in previous studies. This approach, by creating a link between condition-based data analysis and reliability analysis, opens new horizons for condition-based maintenance planning.
-
Presenting an Integrated Physical Asset Management Framework for Oil and Gas Wells through Life -cycle management
Mohammad Yousefi Sheikhrobat, Saeid Ramezani *, Abbas Tolooei Eshleghi, Amir Najafi
Journal of Modern Engineering Management, Autumn 2025 -
Presentation of a Decision-Making Model for Long-Term Storage (Conservation) of Defense Items (Case Study: Armor Equipments)
Mehran Mohammadi, Saeed Khalili *, , Masoud Mosadegh-Khah
Iranian Journal of Supply Chain Management, Winter 2025