<![CDATA[This study presents a comprehensive artificial intelligence–driven predictive maintenance framework tailored to the operational complexities of oil palm processing systems. The proposed architecture integrates Internet of Things (IoT) sensing, edge computing, advanced signal processing, and machine learning within a multi-layer structure comprising data acquisition, preprocessing, AI analytics, decision support, and maintenance execution with continuous feedback. High-frequency sensor data collected from critical equipment such as sterilizers, screw presses, turbines, and digesters are transformed into meaningful representations through time-domain, frequency-domain, and time–frequency feature extraction techniques, including Fourier transforms, wavelet analysis, and statistical descriptors. These features form the basis for robust predictive modeling using both supervised and unsupervised learning approaches, enabling accurate fault detection, anomaly identification, and Remaining Useful Life (RUL) estimation. The framework incorporates advanced prognostic models, including stochastic degradation processes, survival analysis, and deep learning architectures such as Long Short-Term Memory networks, to capture complex temporal dependencies in equipment behavior. To ensure reliability and interpretability, model outputs are complemented with explainable AI techniques and evaluated using rigorous statistical metrics and cost-sensitive optimization strategies. Deployment considerations such as edge inference, model quantization, latency optimization, and system redundancy are integrated to support real-time operation in resource-constrained industrial environments. Furthermore, a closed-loop feedback mechanism enables continuous learning, model adaptation, and performance improvement through data-driven retraining and human-in-the-loop validation. This scalable, production-ready framework transitions oil palm processing to intelligent, condition-based maintenance. Aligned with Industry 4.0, it bridges advanced analytics with practical application to improve predictive accuracy, minimize unplanned downtime, optimize schedules, and drive overall cost savings and sustainability.]]>
