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

Linchao Yang,Chen Li,Wenbing Chang,Shenghan Zhou,Guozhu Jia,Fajie Wei

doi:10.3390/app11083448

Abstract

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.

Highlights

Unmanned aerial vehicles (UAVs) are very versatile and can be used in personal and commercial fields such as aerial photography, agriculture, plant protection, miniature selfies, express transportation, disaster relief, surveying and mapping, and electric power inspection
The traditional UAV fault prediction approach is to monitor a certain flight parameter, and when the parameter exceeds the safe range, or when it is judged that it may exceed the safe range in the future, a risk alarm is issued [2,3,4]
This paper introduces complete-information-based principal component analysis (CIPCA) into the UAV fault prediction and uses a back-propagation neural network (BPNN) to construct the CIPCA-BPNN fault prediction model

Summary

Introduction

Unmanned aerial vehicles (UAVs) are very versatile and can be used in personal and commercial fields such as aerial photography, agriculture, plant protection, miniature selfies, express transportation, disaster relief, surveying and mapping, and electric power inspection. Some scholars use the image returned by the UAV’s own camera to locate and estimate the bounded domain of the UAV’s attitude and to perform fault detection based on the landmark error of the UAV’s tracking image [6]. None of these methods use all of the flight information. The data-driven method uses all the information that the UAV system can collect. The construction of data-driven methods usually includes three steps: first, collecting fault signals; second, extracting fault features; third, identifying and predicting fault [13]. Because the airborne equipment of UAVs record a large amount of flight parameter data in real time, feature extraction and dimensionality reduction of the flight data are very important tasks

Methods

Results

Conclusion