AI-Augmented DFMEA Framework for Predictive Failure Prevention in Heavy-Duty Automotive Lift Systems Using Simulation-Driven Design Validation
DOI:
https://doi.org/10.22399/ijcesen.5509Keywords:
AI-augmented DFMEA, Heavy-duty automotive lifts, Predictive failure prevention, Simulation-driven validation, Prognostics and digital twinsAbstract
Heavy-duty automotive lift systems are subject to coupled structural, hydraulic, tribological, control, and human–machine interaction constraints where failure prevention requires more than just a conventional design review approach based on a checklist. The review explores the potential for enhancing design failure mode and effects analysis (DFMEA) using artificial intelligence, prognostics, digital-twin concepts and simulation-based validation for predictive failure prevention (PFP) of vehicle lifting devices. The review compares the state-of-the-art research literature regarding FMEA enhancement, machine prognostics, assessment of fatigue and welded joints, hydraulic-system modelling, surrogate-based simulation, Bayesian calibration, and digital engineering. The findings suggest that traditional DFMEA is relevant to organizing the causal thinking process, but ordinal risk prioritization does not adequately represent safety-critical risk for lift systems where rare structural or hydraulic failures may account for a disproportionate share of the overall safety risk. When combined with validated finite-element, multibody and hydraulic simulations, the remaining useful life (RUL) and occurrence estimates can be enhanced using AI-based anomaly detection and model updating. However, few public datasets are available, model transferability between lift architectures remains weak, and uncertainty propagation from model assumptions to DFMEA rankings is not yet well formalized. A technically defensible framework must thus have a clear line of sight between failure modes, simulation observables, sensor signals, risk controls and validation evidence. Future research should focus on benchmark datasets, physics-informed prognostics, uncertainty-aware DFMEA scoring and standardization of validation protocols for lifting applications with safety implications.
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