The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction

This paper proposes a complete-information-based principal component analysis (CIPCA)-back-propagation neural network (BPNN)_ fault prediction method using real unmanned aerial vehicle (UAV) flight data. Unmanned vehicles are widely used in commercial and industrial fields. With the development of UAV technology, it is imperative to diagnose predict faults improve their safety reliability. The data-driven provides basis for prediction. A typical complex system. Its data kind high-dimensional large sample dataset, traditional methods cannot meet requirements compression dimensionality reduction at same time. interval compress data, CIPCA reduce compressed then back propagation (BP) failure. Experimental results show that CIPCA-BPNN had obvious advantages over (PCA)-BPNN could accurately failure about 9 s before occurred.