SIC 2026

The presentation addresses the transition from S500 diesel to additive-enhanced S10 diesel in mining operations from the perspective of reliability engineering and decarbonization. The study analyzes how fuel quality directly affects fuel-system reliability, fleet availability and the recurrence of failures associated with contamination, filter clogging and premature component degradation. Based on laboratory analyses and field operating data, it presents the impacts of a higher cetane number and lower sulfur content on combustion efficiency, lower specific fuel consumption and mitigation of greenhouse-gas emissions. The study highlights the integration of RAM, operational performance and ESG as a key element of sustainable, data-driven management for large mining fleets.

Constellation Oil Services’ Reliability and Maintenance Engineering Program was structured to integrate methodologies, indicators and maintenance practices with the goal of increasing the operational reliability of offshore rigs. Level 1 consolidated the program’s foundations through the application of LDA, RCA, FMEA and RCM, together with the creation of strategic guidelines and database standardization. Integrating these elements generated more than 2,455 technical actions, reduced downtime, improved failure analysis and supported data-driven decisions.

Vale Base Metals began building a structured Asset Management process in the corporate environment in 2025, with the goal of increasing operational efficiency. This movement is supported by Integrated Reliability, which includes reviewing normative documents, implementing the Orion system, cost analysis and competency development. The objective is to sustain a reliability center of excellence capable of guiding strategic and operational decisions. The process includes a defined roadmap and IAM representatives distributed across different areas, ensuring alignment and active participation. In 2025, one of the main deliverables is the development of block diagrams for subprocesses in both Canada and Brazil to identify critical points and areas requiring greater attention. In parallel, operational areas, through normative guidelines, are also required to map their subprocesses and highlight the most relevant ones.

This paper presents the development of a Reliability, Availability and Maintainability study (RAM Analysis) and the application of a detailed roadmap for Brazil’s largest oil pipeline, OSBRA. The project was structured in the following stages: identification of the need, data collection and treatment, development of the RAM study, model validation, scenario simulation, conclusions and recommendations. Methodologies and approaches for data treatment are presented, with emphasis on the main challenge in this type of study: poor-quality failure records.

Reliability is essential to the performance of beneficiation plants because unplanned failures directly affect productivity and costs. This paper presents the application of a Reliability, Availability and Maintainability (RAM) analysis in a mineral beneficiation plant to identify critical equipment and propose improvements aimed at increasing operational reliability. The methodology was based on historical failure data, system modeling and simulation of different operational scenarios and maintenance strategies. Based on the RAM results, improvement actions were defined and implemented, including optimization of maintenance plans and review of operational strategies. As a result, the systems analyzed achieved a 12% increase in Mean Time Between Failures (MTBF), along with a 2% gain in physical availability, 33.2 thousand additional tons of production and greater process stability. The results demonstrate the effectiveness of RAM analysis as a tool to support decision-making and continuous reliability improvement in mineral beneficiation plants.

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Preventive maintenance is essential to ensure the reliability and operational efficiency of instrumentation systems and electric motors. Through detailed reliability analysis using LDA life data, it was possible to optimize the frequency of major maintenance shutdown plans, reducing costs and increasing equipment availability. This study addresses best practices and strategies for implementing an effective preventive-maintenance program, focusing on optimizing maintenance intervals and maximizing asset useful life.

Field occurrences related to insufficient torque-transmission capacity in a commercial-vehicle clutch system were reported under initial operating conditions. To address the issue, a systematic reliability analysis based on controlled bench tests was conducted, focusing on torque-transmission performance and its statistical distribution. By applying reliability curves (Weibull and Normal analyses) to characterize the probability of torque loss, design and process parameters with potential to improve transmission capacity were identified. Based on the experimental results and statistical modeling, optimization actions increased the reliability level of the specified torque from approximately 80% to 99.9%. The new design was subsequently validated under real operating conditions with the customer, confirming the robustness and consistency of the field results. The study demonstrates the effectiveness of integrating reliability engineering and experimental validation to ensure the functional robustness of automotive transmission systems.

Stock calculation for vertical mill drive gearboxes. In November 2025, it was identified that there was no spare gearbox for the Vertimill vertical mill drive and that minimum and maximum stock parameters had not been registered in Vale’s CMMS (Computerized Maintenance Management System). Since the plant currently operates with nine gearboxes, all critical to the process, it was necessary to define the ideal number of units to be held in stock, considering lead time, equipment value and reliability. The study used a four-year historical database of gearbox replacements. With the support of Orion, estimates of equipment life, failure probabilities over different time horizons and, consequently, the minimum stock required to ensure a given service probability were obtained. The resulting report was presented to the responsible manager, who used the analyzed data to determine the ideal number of gearboxes to keep in stock, considering stockout risk and the desired probability of success for operational continuity. The greatest challenge was treating a database containing inconsistencies. However, through careful data cleaning and the analytical support provided by Orion, it was possible to obtain reliable results and establish a robust parameter for managing Vertimill vertical-mill gearbox inventory.

This paper presents the application of RAM (Reliability, Availability and Maintainability) methodology to the agricultural Harvesting, Transshipment and Transportation (CTT) system in the sugarcane and ethanol sector. Using historical work-order data from the previous five years (2019–2024), a quantitative analysis was performed on three service fronts composed of sugarcane harvesters, tractors, transshipment equipment and trucks with trailers. Modeling was carried out using Reliability Block Diagrams (RBD), separating assets into compartments. Reliability, maintainability and availability were calculated for each front, together with projected average availability for the next five years. The study provides insights into asset criticality and strategies for allocating assets so that equivalent performance can be achieved. The results provide technical support for optimizing sugarcane delivery to the plant and improving CTT-system performance.

Operational readiness management is one of the pillars for ensuring a safe transition between the implementation and operation phases of large industrial projects. In many projects, however, the absence of a structured reliability-based approach creates operational risks and inefficiencies, premature asset failures and difficulty sustaining performance after commissioning. This paper presents the integrated application of classic reliability-engineering tools such as HIRA, Bow-Tie, FMEA, RCM and statistical analyses, together with the definition of Maintenance Strategies in operational-readiness management. The proposed methodology seeks to anticipate failures, establish preventive barriers and strengthen decision-making during project, Opex variation, implementation and handover-to-operations phases. The result is a significant improvement in operational predictability, control of critical risks and maturity of the operational-readiness process, helping reduce early failures, increase asset availability and improve alignment between project and operations areas, including PCM. The work demonstrates that systematic incorporation of reliability tools raises the level of technical and operational governance in projects.

This paper presents the application of a RAM (Reliability, Availability and Maintainability) analysis in a hydroelectric power plant composed of three generating units, with the objective of optimizing operational performance, reducing costs and supporting data-driven decision-making. The methodology used specialized tools — BlockSim and Weibull++ — to build Reliability Block Diagrams (RBD) and statistically model failure patterns using historical time-to-failure (TTF) and time-to-repair (TTR) records. Two scenarios were evaluated: systematic preventive maintenance and reliability-based preventive maintenance. The results demonstrated a gain of 218 hours of operational availability and a positive financial impact from increased energy production and lower corrective-maintenance costs. Bad-actor analysis identified the Rotor Braking and Lifting System, Turbine Cover Drainage System and Flow Instruments as the main failure drivers. Adopting the reliability-based strategy reduced failures in these systems by 21%, 31% and 32%, respectively. The study also includes a Life Cycle Cost (LCC) Analysis, which identified the sixth year as the minimum total cost-of-ownership point. It concludes by recommending a continuous RAM-analysis program, prioritization of preventive and predictive maintenance, and investment in digitalization using smart sensors and Machine Learning for failure prediction.

This paper presents a Reliability Solutions HUB Across the Life Cycle of Offshore Industrial Assets, integrating RAM analysis (Reliability, Availability and Maintainability), Artificial Intelligence and Bayesian Inference applied to asset management. The portfolio consists of five innovative solutions: RAM modeling of the flare ignition system, an intelligent evaluator for maintenance plans, dynamic Bayesian modeling to incorporate predictive data, RAM analysis applied to the water-injection system, and maintenance optimization based on advanced models for engines and generators. The initiatives span all stages of the asset life cycle, from design through mature operation and decommissioning, enabling more precise decision-making, lower costs and increased availability and safety. The results show significant gains, including reductions of up to 40% in maintenance labor hours and optimization of reliability strategies in critical systems. The work highlights the value of multidisciplinary, data-driven integration, promoting a culture of excellence in asset management and supporting strategic, robust and safe decisions in the demanding offshore environment.

Human Reliability Analysis (HRA) has become one of the most effective approaches for preventing human failures in critical activities, especially in industrial environments where small performance degradations can lead to high-severity events. This paper presents the structured application of HRA as a practical tool to transform task mapping into tangible risk-reduction results. The methodology integrates three analytical pillars: HTA (Hierarchical Task Analysis), SHERPA (Systematic Human Error Reduction and Prediction Approach) and PSF (Performance Shaping Factors), making it possible to identify tasks with error potential, characterize scenarios, calculate the probability of human failure and prioritize actions objectively based on impact. Detailed analysis of dozens of operational processes showed that between 13% and 50% of the basic tasks evaluated had significant potential for human failure. Applying HRA supports procedure review, process redesign, improved operating practices, stronger training and implementation of specific recommendations, directly reducing the probability of occupational and process events, preventing fatalities and strengthening safety barriers. The study shows that HRA goes beyond observing human behavior: it changes how organizations understand real work, enabling data-based decisions, structured interventions and measurable results. Consistent application of the methodology demonstrates that safer systems are built when doing the right thing is easier, making mistakes is harder and performing dangerous actions is impossible, reducing risk effectively and sustainably.

This paper presents an integrated approach to evaluating useful life, quantitative reliability and roller-renewal strategy for Ferroport belt conveyors, combining reliability-engineering fundamentals, asset management, Lean Manufacturing, defect elimination, risk analysis and life-cycle cost. The objective was to evolve management of these components from a predominantly preventive logic — but one requiring greater predictive and decision-making accuracy to reduce recurring corrective interventions — toward an approach driven by predictability, condition, risk, performance, remaining life and assurance of reliability.

Our client, a large pulp-and-paper company, implemented a new wood-chipping line. During the first months of operation, several problems occurred and a complete reliability analysis was conducted to guide the resources that should be deployed. Extended Crow-AMSAA analysis, GRP analysis and shift-performance analysis (LDA with repair times) were used to help the client determine the best path to achieve the expected performance for this line.

The project aimed to support deployment of the organization’s strategic map by adapting the Maintenance Strategy in a sustainable way and balancing asset cost, risk and performance. It sought to ensure achievement of the production plan through decisions structured around Reliability Engineering, focused on generating value throughout the life cycle in accordance with ISO 55000 principles.

This paper presents the application of the Crow-AMSAA model to optimize the overhaul interval of locomotive diesel engines, seeking to balance cost, execution capacity and operational reliability. Analysis based on actual failure data made it possible to identify the optimal overhaul point, generating significant gains in availability, cost reduction and environmental sustainability. The study reinforces the importance of integrating engineering, maintenance and operations to make more accurate and sustainable decisions.

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The paper presents the main results and lessons learned from implementing Orion integrated with SAP S/4HANA, highlighting how this digitalization generated real improvements in reliability, information standardization and agility in maintenance decision-making. The project consolidated data, improved traceability and strengthened governance of engineering processes. The presentation shows advances achieved through modules such as RCM, RCA, RGA, failure prediction and risk management, among others, which now support more complete analyses, help anticipate problems and support criticality-based prioritization. These modules illustrate in practice how the solution began transforming data into reliable insights to guide maintenance actions. By integrating Orion and SAP, the project contributed to moving the area toward a more predictive, strategic and evidence-based model. The demonstration clearly shares how this journey strengthened the reliability culture and increased the maturity level of the organization’s asset management.

This paper consolidates strategic demands from the Engineering and Information Technology areas operating at a mining company’s port unit, focusing on strengthening asset reliability, improving maintenance performance and increasing operational efficiency, supported by intensive use of Artificial Intelligence (AI) applied to reliability-engineering activities throughout 2025.

This paper aims to carry out a Reliability Engineering study applied to overhead cranes in the Potroom of the Reduction area at the São Luís/MA unit. The first stage consists of applying RAM (Reliability, Availability and Maintainability) methodology to analyze asset reliability, availability and maintainability based on operational and failure history. As a complementary stage, LCCA (Life Cycle Cost Analysis) is planned for systems identified as critical in the RAM analysis. The objective is to determine the optimal replacement or renewal interval for these systems, providing a technical and economic basis for strategic decisions related to renewal of the overhead cranes or their subsystems, seeking operational-efficiency gains and continuous process improvement. The study also includes a review of current maintenance tasks using RCM (Reliability-Centered Maintenance). This stage will focus on systems classified as critical in the RAM analysis to evaluate and optimize maintenance strategies, making them more closely aligned with failure modes, asset functions and operational performance objectives.

The article’s main objective is to explore the impact of NR10 compliance adjustments on reliability prediction for industrial machines. It demonstrates how Life Data Analysis (LDA) can be applied to anticipate failures and optimize maintenance planning, ensuring continued compliance with safety standards and operational-efficiency requirements. Another objective is to apply Reliability Engineering to anticipate failures not covered by the asset’s original maintenance strategy.

This paper presents a Degradation Analysis of brake-pack wear on CAT 777G mining trucks operating in an open-pit mine. The study covers a fleet of 14 trucks, totaling 56 brake packs analyzed, since each truck has four independent assemblies. Brake packs are critical components for operational safety and asset availability and are exposed to high levels of thermal and mechanical load due to the severe operating regime.

Reliability-Centered Maintenance (RCM) is widely used to define maintenance strategies for industrial assets; however, large-scale application and conversion of results into executable plans within maintenance-management systems remain challenges for many organizations. This paper presents the application of Artificial Intelligence to support the development of RCM studies, associated with a structured mapping process for implementing maintenance strategies in the CMMS. The approach was initially applied to representative asset types, structuring studies into standardized modules that can be replicated across similar assets without loss of methodological quality. The results demonstrate improvements in study-development speed, standardization of analyses and greater ease in implementing maintenance strategies in corporate systems.