Journal of Quality Engineering and Management

Abstract Purpose: This paper aims to estimate the parameters of the Weibull distribution using a genetic algorithm and compare its performance with traditional estimation methods.
Methodology: A simulation study was conducted under different sample sizes and censoring levels. The genetic algorithm was applied to maximize the likelihood function.
Findings: The results show that the genetic algorithm provides more accurate and stable parameter estimates compared to the maximum likelihood method, especially in the presence of censored data.
Originality/Value: This study presents a novel application of genetic algorithms in reliability analysis, demonstrating their effectiveness in parameter estimation for censored datasets.

Abstract Purpose: 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.
Methodology: 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.
Findings: 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.

Abstract Reliability studies are an essential part of every management program for equipment maintenance. As systems become more complex, maintenance strategies become critical for making sustainable management decisions. Unexpected failures in a system can be the primary reason for the poor performance of industrial machinery and equipment. Various fault resistance mechanisms are utilized to make critical decisions for the system. In the present paper, a two-parameter Weibull distribution approach is considered to evaluate the heat exchanger datasets in the petrochemical industry using Isograph Hazop + v7.0 software. As the effective execution of a system depends on its reliability and planning under suitable conditions, this paper presents a strategy for finding the reliability to calculate the preventive maintenance intervals in actual systems. This approach leads to fewer number of inspections and fewer repair activities, higher safety and reliability of industrial units and also, higher economic benefit.