Computational Modeling of Failure Mechanisms in Mechanical Systems: Applications For Energy and Industrial Sectors

Abstract

This study examined computational modeling of failure mechanisms in mechanical systems operating within energy and industrial sector applications using a quantitative, explanatory case study design. A panel dataset comprising 412 component-level units embedded within 168 mechanical assets was analyzed across 9,214 time-indexed observation windows spanning an average observation horizon of 17.2 months. Failure severity was operationalized as a continuous composite outcome to preserve degradation gradients rather than binary failure states. Descriptive analysis showed that failure severity exhibited strong right-skewness, with a median value of 0.61 and a 90th percentile of 1.82, indicating concentration of degradation within a limited subset of operating windows. Multivariable mixed-effects regression was applied to quantify the influence of operational exposure and degradation constructs while accounting for asset-level heterogeneity. The final model achieved a marginal R² of 0.48 and a conditional R² of 0.62, confirming substantial explanatory power from both fixed effects and asset-level random effects. Condition monitoring indicators demonstrated the strongest association with failure severity (β = 0.356, p < 0.001), followed by cyclic loading (β = 0.287, p < 0.001) and thermal exposure intensity (β = 0.214, p < 0.001). Contact stress (β = 0.169, p < 0.001) and time-under-load (β = 0.132, p < 0.001) also retained statistically significant effects. Operational regime analysis showed that continuous-duty components exhibited higher severity than intermittent-duty units (β = 0.118, p < 0.001), while cyclic-duty operation also increased severity (β = 0.064, p = 0.040). Interaction testing revealed amplification effects for combined thermal exposure and cyclic loading (β = 0.072, p = 0.013), indicating non-additive degradation behavior. Robustness checks demonstrated coefficient stability within ±5% under alternative specifications, and time-based holdout validation resulted in a modest 6.2% increase in prediction error. Overall, the findings established a statistically stable and interpretable framework linking monitoring-derived degradation signals and core exposure mechanisms to continuous failure severity within operational mechanical systems.