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

• Published in: Applied sciences Vol. 11; no. 8; p. 3448
• Main Authors: Yang, Linchao, Jia, Guozhu, Wei, Fajie, Chang, Wenbing, Li, Chen, Zhou, Shenghan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2021
• Subjects: Back propagation; Back propagation networks; BP neural network; complete-information-based principal component analysis; Complex systems; Compression; Data compression; Decomposition; Eigenvalues; Fault diagnosis; fault diagnostics and prognostics; Flight; interval data; Machine learning; Neural networks; Predictions; Principal components analysis; Signal processing; UAV flight data; Unmanned aerial vehicles
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11083448

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