Reducing locomotive maintenance costs with intelligent software

This article introduces novel computer software designed to optimize maintenance schedules for traction rolling stock (TRS). The software analyzes TRS failure/breakdown data, considering reliability and cost-efficiency parameters, to recommend optimal inter-repair mileage standards. It analyzes locomotive component reliability indicators, investigates failure distribution hypotheses, and offers cost-effective recommendations for adjusting maintenance intervals. This data-driven approach addresses the limitations of traditional, generalized maintenance schedules by dynamically adapting to specific operating conditions and leveraging statistical models like Weibull, exponential, and normal distributions to predict failure-free operation. The software aims to improve fleet management by reducing downtime, minimizing unscheduled repairs, and lowering overall maintenance costs.

Purpose. This article aims to present computer software designed to process traction rolling stock (TRS) failure data and optimize inter-repair mileage standards, considering both reliability and cost-effectiveness parameters. The software analyzes locomotive component reliability indicators, investigates failure distribution hypotheses within locomotive units, and generates recommendations for optimizing inter-repair mileage while minimizing maintenance and repair costs.

Materials and methods. Methods and Software Description: The software developed in this study employs a multi-stage analysis process to optimize the standards for inter-repair mileage of locomotives. The core methodology is based on the statistical analysis of reliability indicators, including failure rates and the time between failures for individual components. Data collection is performed by sorting failures according to their type and nature, allowing for the identification of recurring patterns and failure modes.

Results. This study has demonstrated the effectiveness of a software-based approach for optimizing inter-repair mileage standards for locomotives. By leveraging statistical failure analysis and integrating economic cost models, the software reduces both the frequency of unplanned repairs and overall maintenance costs. The improvements in locomotive reliability and operational efficiency underscore the value of this tool for railway operators. Furthermore, this software, optimizing inter-repair mileage norms with consideration for reliability indicators and calculating economic impact aimed at reducing downtime, offers significant potential for practical application. Its ability to analyze failure data and predict potential issues allows for proactive maintenance scheduling, minimizing disruptions to operations and maximizing resource utilization. This contributes directly to improved cost-efficiency by reducing the need for reactive repairs and associated expenses.