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This paper develops a staged robustness analysis framework that connects Structural Equation Modelling (SEM), Ordinary Least Squares (OLS), and Double Machine Learning (DML) for survey-based latent-construct research, demonstrated on a FinTech Digital Customer Intimacy survey model. The framework provides a reusable template for researchers to assess stability of findings across different estimation methods.
The paper proposes a reliable fault diagnosis method using a belief rule base with robustness analysis, addressing sensor reliability issues, and validates the approach on WD615 diesel engine and bearing datasets.