Enhancing Transparency and Accountability in Predictive Maintenance with Explainable AI

Predictive maintenance is a critical aspect of industrial operations, enabling proactive identification and mitigation of potential failures in machinery and equipment. However, the widespread adoption of AI-driven predictive maintenance solutions has been hindered by the opaque nature of many machines learning models, raising concerns about transparency, accountability, and trust. This research aims to address these challenges by developing explainable AI techniques for predictive maintenance in industrial systems. By integrating interpretability methods with advanced predictive models, we seek to enhance the transparency and interpretability of AI-driven maintenance decisions. Our proposed methodology combines state-of-the-art machine learning algorithms with local and global explainability techniques, such as LIME, SHAP, and feature importance analysis. Through extensive experiments on real-world industrial data, we evaluate the performance of our explainable AI models and demonstrate their ability to provide insightful explanations, enabling domain experts to understand the underlying reasoning and critical factors contributing to maintenance predictions. Furthermore, we explore the impact of explainable AI on improving trust, accountability, and adoption of AI systems in industrial predictive maintenance scenarios.