Advanced weibull reliability analysis for industrial pumping systems: An integrated framework for predictive maintenance optimization

Industrial pump systems are considered crucial in many sectors, and the cost of operation and maintenance consumes between 40% and 50% of the total cost of operation. The traditional method of preventive and corrective maintenance contains serious weaknesses, such as unnecessary maintenance and unplanned breakdowns. A new approach for the analysis of reliability of industrial pump systems using six higher-order methods geared towards the task is proposed in this study. On the basis of the available 100 failure data samples for the petrochemical centrifugal pump, the study applies the following techniques: (1) Bootstrap resampling to estimate the uncertainty margins in a more precise manner compared to the maximum likelihood estimation technique, indicating the confidence intervals to be substantially narrower; (2) Condensed reliability models for the calculation of the residual life based on the age parameter; (3) competing risks analysis for the illustration of five distinct failure modes, each associated with a specific hazard rate function; (4) mixed Weibull modelling for the representation of bimodal failure distributions; (5) cost-oriented optimization to demonstrate the potential for cost savings compared to run-to-failure strategies; and (6) dynamic analysis of reliability with time-varying confidence intervals. The proposed Baseline Weibull distribution (β=2.69, η=2481.73 hours) proves the wear-out process’s validity, being fully tested for goodness of fit (p-value for K-S test=0.869, A-D statistic=0.590). Completed analysis on competing risks identifies five prevalent failure causes, for which the sample size is statistically justified (n>5, based on the asymptotic properties of the MLE estimator). The integrated approach obviates the weaknesses inherent in traditional, uni-directional solutions and offers actionable advice for industrial maintenance planning based on economic feasibility.