How can the reliability of industrial equipment be better predicted? This research addresses the challenge of estimating distribution parameters for failure data in industrial equipment subject to two failure modes when the cause of each failure is unknown. The study is focussed on improvement of accuracy and reliability of industrial instruments. The research developed a loglikelihood method which was then tested on generated mixed failure mode data. Shortcomings of this method triggered the development of two additional methods, MINESS and MINESS+, which minimize the error sum of squares of the reliability function, after separating the failure data into two sets. This study offers improved methods for reliability analysis, contributing to better maintenance strategies and reduced downtime in industrial settings.
The article’s focus on estimating distribution parameters for failure data aligns with the Journal of Quality in Maintenance Engineering's emphasis on improving maintenance strategies and ensuring equipment reliability. The development of new methods for analyzing mixed failure mode data contributes directly to the journal's goal of advancing quality and effectiveness in maintenance engineering practices. The statistical nature of the methodologies also connects to the quantitative focus of the journal.