Classification of helicopter’s typical flight state based on threshold
Open Access
- 1 November 2021
- journal article
- Published by IOP Publishing in IOP Conference Series: Materials Science and Engineering
- Vol. 1207 (1)
- https://doi.org/10.1088/1757-899x/1207/1/012024
Abstract
Dividing the 37 flying state of a certain line number helicopter. Firstly, dividing the helicopter rotation and single-engine flight. Secondly, performing preliminary state division for the remaining samlpes, the specific division of yaw angle, helicopter flight altitude and indicated air speed are different states, the least squares polynomial method is used for smoothing respectively. Calculating the extreme value of each parameter data, with the difference value of the extreme value of the parameter data being less than 10 as the limiting condition, dividing the original data segment into non-turning, level flight and steady speed state. The remaining sampling points are in the state of unsteady turning and non-level flight. Taking the difference value 0 as the limiting condition, further divide the non-steady speed and non-level flight state. Dividing the state of turning and non-turning, level flight, ascent and descent, steady speed, increase speed and deceleration state, which is the preliminary division state. Finally, dividing the near-ground and non-near-ground, classifying the helicopter status according to the height threshold, and analyze the accuracy of the classification results. The results show that this method is versatile, can quickly divide helicopters with different flight complexity, and has high accuracy.This publication has 4 references indexed in Scilit:
- Multi-bearing remaining useful life collaborative prediction: A deep learning approachJournal of Manufacturing Systems, 2017
- Damage tolerance and classic fatigue life prediction of a helicopter main rotor bladeMeccanica, 2015
- Machine health prognostics using survival probability and support vector machineExpert Systems with Applications, 2011
- A Self-Adaptive RBF Neural Network Classifier for Transformer Fault AnalysisIEEE Transactions on Power Systems, 2010