Aug 2026· Engineer· Vol 7, pp. 383· 0 citations· 59 references
Abstract
The operational dynamics of the steel industry constitute one of the work systems with the highest severity and accident rates. To address this, this research multidimensionally evaluates and stochastically predicts the preventive capability of the safety system in a steel plant. Using a quantitative, evaluative, and longitudinal three-phase design, the retrospective evaluation of nine preventive variables was employed using Six Sigma metrics (DPMO, Z, Y), along with the evaluation of overall performance through the Geometric Capability Indicator (GCI) and the Arithmetic Capability Indicator (ACI), and the stochastic modeling of the process using Markov chains. It was demonstrated that evaluating processes in isolation hides structural inefficiencies, as four variables showed an Excellent individual performance (Z≈6.0), but the comprehensive multivariate evaluation revealed a Deficient systemic state (GCI of 0.471 and ACI of 0.493). Furthermore, Markov modeling on the compliance of the process management index predicted a 100% probability of long-term stagnation in a deficient absorbing state (x1=1). It is concluded that the proposed method functions as a rational anticipation system that provides potential managerial benefits by offering early warning indicators of operational degradation, supporting corrective decision-making on unstable preventive indicators.
The article proposes a methodology that integrates methods, models, algorithms, and tools for predictive strategic risk assessment in fuel and energy sector (FES) companies to support managerial decision-making under uncertainty in the short, medium, and long term . The methodology rests on scientifically grounded premises, assumptions, and constraints: the financial position of an FES company is treated as an indicator of the cumulative impact of strategic threats, and the indicators describing this position are modeled by a joint lognormal distribution. The study provides a theoretical rationale for using the mode of the multivariate distribution of FES companies’ financial indicators as the industry benchmark vector. To improve the quality and reliability of the methodological basis for quantitative strategic risk assessment, it substantiates an integrated measure based on the Mahalanobis distance. This distance captures the deviation of a multivariate vector describing an FES company’s financial position from the industry benchmark. The article also develops a method for determining strategic risk levels by identifying statistically significant deviations of companies’ financial indicator vectors from this benchmark using the proposed integrated measure. Simulation modeling is used to analyze changes in strategic risk levels under different scenarios of financial indicator dynamics in FES companies. The proposed Monte Carlo algorithm uses revenue as the controllable stress-testing variable and evaluates changes in the integrated strategic risk measure under controlled revenue variations and simulated uncertainty. Simulation results an therefore serve as a basis for developing strategic threat profiles and assessing strategic risks in fuel and energy sector companies. Calculations based on model-generated data demonstrate the viability of the proposed methodology, and its testing confirms the feasibility of practical implementation.
S. I. Turko, M. A. Kirkin· Strategic decisions and risk...· 0 citations
Purpose of research
. The purpose of research is to develop and substantiate a logic-probabilistic model for managing maintenance and repair processes of ship technical systems, taking into account the stochastic dependence between component failures using copula theory. The study also aims to quantitatively assess the impact of external operational factors on equipment reliability and on the accuracy of critical failure prediction
Methods
. The study applies the logic-probabilistic reliability analysis method (fault tree analysis) to formalize the structure of a ship system and describe the conditions leading to a critical system failure. To model statistical dependence between failures of interconnected components, the Clayton copula is employed, enabling the representation of strong lower-tail dependence. Failure probabilities of individual components are determined based on the exponential time-to-failure distribution. A corrective function reflecting the impact of exogenous factors (humidity, salinity and vibrational load) is incorporated into the model. Empirical validation is performed using operational data from 15 vessels of Project 22870 for the period 2020-2024, applying absolute and relative error metrics.
Results
. The results show the accounting for the dependence between the cooling pump and the generator increases the estimate annual probability of a critical system failure from 28.11% to 37.04% (a 31.77% relative increase). The probability of their joint annual failure reaches 17.52%, which is more than twice the estimate obtained under the assumption of statistical independence. Model validation demonstrates a deviation of less than 2.88% from observed data.
Conclusion
. The proposed model provides a more accurate assessment of operational failure risks and can serve as a tool for adaptive maintenance planning, as well as integration with digital twins and predictive maintenance systems in the shipbuilding industry.
I. D. Makarova, I. Bondareva· Proceedings of the Southwest...· 0 citations
Accurate assessment of accident severity and regulatory deficiencies is essential for improving safety management and intelligent decision-making in complex industrial systems. This study proposes an interpretable machine learning framework to evaluate the influence of regulatory variables on coal mine accident severity by integrating data-driven prediction with SHAP-based feature attribution. Based on grounded-theory analysis of 382 accident investigation reports, more than 800 textual statements were condensed into 62 initial concepts, 13 secondary indicators, and five categories of regulatory factors, including design schemes, management systems, technical documents, organizational measures, and process methods. These variables were encoded and incorporated into Logistic Regression, C4.5, CART, CHAID, and Random Forest models for comparative analysis. Experimental results demonstrate that the Random Forest model achieves the best predictive performance in terms of accuracy and AUC, while SHAP analysis provides quantitative interpretation of the contribution and interaction of regulatory variables. The findings indicate that organizational measures, technical documentation, process methods, and management systems are the dominant determinants of accident severity, enabling transparent risk assessment and targeted intervention strategies. The proposed framework establishes an effective methodology for interpretable predictive analytics, intelligent safety monitoring, and data-driven decision support in complex engineering environments, offering valuable references for distributed sensing systems, industrial information fusion, and intelligent monitoring architectures related to Electromagnetic Waves, Antennas and Propagation engineering applications.
M. Li, X. Zhou, H. Li· Advanced Electromagnetics· 0 citations
This study investigates predictive modeling of failure states in manufacturing systems using artificial intelligence in the context of sustainable maintenance and Industry 4.0. The proposed methodological framework is based on historical operational and maintenance data from a single manufacturing device, encompassing multiple process and operational signals such as vibrations, temperature, electric current, and operational logs. The aim is to predict failure within a short-term horizon to support maintenance planning. The article compares Random Forest and XGBoost algorithms at different prediction horizons (8 h and 16 h) to identify the trade-off between classification accuracy and lead time for maintenance planning. Model outputs are analyzed using explainable artificial intelligence and transformed into a risk index compatible with the FMEA methodology. The practical contribution of the proposed approach is illustrated through a scenario-based what-if assessment of potential sustainability impacts, particularly in terms of estimated reductions in unplanned downtime, material waste, and energy consumption. The results point to the potential of integrating AI-supported maintenance as a tool for increasing the reliability and sustainability of manufacturing systems. The novelty of the proposed framework lies in the integration of predictive maintenance, explainable artificial intelligence, replay-based maintenance assessment, dynamic FMEA risk assessment, and sustainability impact quantification into a unified decision-support framework.
Miroslav Rakyta, Peter Bubeník, Vladimíra Biňasová et al.· Electronics· 0 citations
Measuring and controlling dimensional accuracies are very critical in steel manufacturing to meet high specification requirements of customers and to provide competitiveness. MPL Steels, being one of the major steel producers in India, have been experiencing great scrap rates of 9-11% in the context of dimensional inconsistency amidst roller wear, mill instability and cutting errors, a fact that has caused massive loss of finances and Occasional operational difficulties. The purpose of the study was to design and institute an integrated Lean Six Sigma system with the increased predictive maintenance and digital optimization capability to methodically minimize defects and improve process capability in MPL Steels in a sequential manner. Applying the DMAIC Six Sigma approach, the identification of the root causes was fulfilled by means of Pareto, regression, and cause-effect tools applied to a big amount of production data. The important interventions were taken such as monitoring of roller wear using IoTs, auto-adjustment of cutting parameters using an AI program, a prediction-based maintenance schedule, and thorough training of operators, and the pain points were monitored using a statistical process control chart. After its implementation, MPL steels realized 55.4% reduction in its thickness-related scrap, which reduced the scrapping rates to 4.1% downturn 9.2%, whereas process capability (Cpk) also improved up to 1.33 up turned 0.82. Reduction in machine downtimes was 66.7%, the total defects decreased by 61.5%, and the annual saving was much more than half a million dollars. The findings show that a combination of Lean Six Sigma and Industry 4.0 technologies can be used to make significant improvements in quality, operational efficiency, and sustainable competitiveness in a mid-sized steel manufacturing that will serve as a basis of long-term, data-driven continuous improvement.
Thimothi Banala, H. Saad· World Journal of Advanced Re...· 0 citations
This research aims to present the Framework for Enhancing Network Integrity eXcellence (FENIX) model, integrating technological, organizational, human, and economic dimensions to enhance risk assessment in complex socio-technical systems.
The FENIX model employs fuzzy logic, multi-criteria prioritization, and non-additive aggregation methods to enhance risk assessment in complex socio-technical systems. Its applicability is demonstrated through a case study involving the introduction of a new aircraft.
The findings indicate that technological risks can be reduced through diagnostic and monitoring systems, whereas operational and human factors continue to play a significant role. Training, communication, and fatigue management are essential for minimizing systemic vulnerability. The economic dimension highlights the feasibility of reconciling mitigation effectiveness with financial sustainability.
The methodology assists decision-makers by optimizing the RPN, improving RTO reliability, and incorporating ERI and EPNmetrics, thereby allowing organizations to align risk management strategies with operational resilience and financial constraints.
In contrast to conventional FMEA, the FENIX framework incorporates uncertainty, nonlinear interdependencies, and cost–benefit considerations, offering a more comprehensive and flexible approach to risk prioritization and resource allocation.
Giuseppe Caristi, Daniela Barba, Maria Frasca et al.· Management Decision· 0 citations