AI-Driven Solutions for Predictive Aircraft Maintenance using Sensor Data

Predictive maintenance (PdM) uses critical functions to lower aircraft maintenance duration and create higher operational safety through early failure detection. Regular maintenance approaches regularly respond after problems arise thus leading to both unexpected maintenance work and expensive repairs. The authors develop a sensor data performing predictive aircraft maintenance strategy through artificial intelligence systems. Real-time sensor data collection and preprocessing occurs first in this model before machine learning algorithms including Random Forest, Support Vector Machine (SVM) and Neural Networks take over failure prediction processes. The research evaluates predictive aircraft maintenance models through accuracy, precision and recall and F1 score assessment using more than 10,000 aircraft engine and avionics system data points. The experimental results confirm that AI-based algorithms deliver superior results beyond traditional methods because they attain 92.5% accuracy alongside 89.3% recall during fault detection operations. The proposed method dramatically lowers the requirement for human inspections and alerts operators early about system breakdowns which enables more effective maintenance planning and reduces operational expenses. The presented work brings forth an effective solution to improve predictive maintenance capabilities for aviation applications.