Explainable Artificial Intelligence (XAI) as a Trust and Security Enabler in Predictive Maintenance Systems: An Empirical Study

Artificial Intelligence (AI)–driven predictive maintenance (PdM) has significantly enhanced asset reliability and operational efficiency in Industry 4.0 environments. However, the opacity of complex machine learning models raises concerns regarding trust calibration, accountability, and cybersecurity resilience. This study empirically investigates the role of Explainable Artificial Intelligence (XAI) in enhancing maintenance engineers’ trust and strengthening security robustness in predictive maintenance systems. Using a mixed-method design involving survey data (n = 214 maintenance professionals) and an experimental simulation comparing black-box and SHAP-enhanced models, the findings demonstrate that explainability significantly improves perceived transparency (β = .71, p < .001), trust (β = .63, p < .001), and adoption intention (β = .52, p < .01). Moreover, XAI-enhanced systems exhibited superior adversarial anomaly detection rates (88% vs. 71%) and reduced forensic investigation time. Mediation analysis confirms that trust partially mediates the relationship between transparency and adoption. The study contributes to the literature by integrating trust and security into a unified socio-technical framework and demonstrates that explainability functions as both a psychological assurance mechanism and a cybersecurity amplifier in predictive maintenance ecosystems.

Explainable AI, Predictive Maintenance, Trust in Automation, AI Security, Industry 4.0, Adversarial Robustness

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