A Study on Failure Prediction of Airborne Electronic Equipment

2007 ◽  
Vol 353-358 ◽  
pp. 2892-2895
Author(s):  
Hong Peng Li ◽  
Yu Ting He ◽  
Rong Shi ◽  
Heng Xi Zhang ◽  
Feng Li
Keyword(s):  
Support Vector Regression ◽  
Failure Probability ◽  
Failure Prediction ◽  
Working Time ◽  
Electronic Equipment ◽  
Support Vector ◽  
Airborne Radar ◽  
Equipment Failure ◽  
Maintenance System ◽  
Auto Regressive

The mostly working time of airborne electronic equipment is under preliminary depletion failure phase, and inspection & maintenance at intervals can’t lower the failure probability. In this paper, the law of airborne electronic equipment failure is introduced firstly. Then, methods for failure prediction are summarized and analyzed. Finally, an example for predicting the airborne radar failure using the Auto-Regressive (AR) and Support Vector Regression (SVR) model is presented. On this basis, it is possible to achieve the goal that increases the reliability in working phase and establish a more scientific maintenance system and to assure the safety of airborne electronic equipment.

Electronic Equipment Combination Fault Prediction Technology Research Based on LSSVM-HMM

2014 ◽  
Vol 687-691 ◽  
pp. 978-983
Author(s):  
Yan Ping Tian ◽  
Xiao Hui Ye ◽  
Ming Yin
Keyword(s):  
Failure Prediction ◽  
Prediction Method ◽  
Electronic Equipment ◽  
Condition Based Maintenance ◽  
Fault Prediction ◽  
Support Vector ◽  
Technology Research ◽  
State Prediction ◽  
Fault State ◽  
Fault Information

In order to solve the problem of complicated electronic equipment structure, inadequate fault information, hard to predict the fault and the existing failure prediction method cannot predict the state of the electronic equipment and other issues directly, we propose a combination failure prediction methods of least squares support vector machine (LSSVM) and hidden Markov model (HMM) based on Condition Based Maintenance (CBM). First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.

scholarly journals A SEASONAL AUTO-REGRESSIVE MODEL BASED SUPPORT VECTOR REGRESSION PREDICTION METHOD FOR H5N1 AVIAN INFLUENZA ANIMAL EVENTS

2011 ◽  
Vol 10 (02) ◽  
pp. 199-230 ◽  
Author(s):  
JIE ZHANG ◽  
JIE LU ◽  
GUANGQUAN ZHANG
Keyword(s):  
Time Series ◽  
Avian Influenza ◽  
Support Vector Regression ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Research Area ◽  
Support Vector ◽  
Fast Fourier Transformation ◽  
Epidemic Disease ◽  
Auto Regressive

The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support vector regression (SAR-SVR) model which combines the seasonal auto-regressive (SAR) model with a support vector regression (SVR) model to address this prediction problem to overcome existing limitations. Fast Fourier transformation is also merged into this method to identify the latent seasonality inside the time series. The experiments demonstrate that the developed SAR-SVR method out-performs SVR, Box-Jenkins models and two layer feed forward NN model-both in accuracy and stability in the avian influenza epidemic disease time series prediction.

Simulation and system identification of helicopter dynamics using support vector regression

2015 ◽  
Vol 119 (1222) ◽  
pp. 1541-1560 ◽  
Author(s):  
S. Manso
Keyword(s):  
System Identification ◽  
Dynamic System ◽  
Support Vector Regression ◽  
Network Architecture ◽  
Input Data ◽  
Support Vector ◽  
High Fidelity ◽  
Flight Data ◽  
Auto Regressive ◽  
Simulation Results

AbstractThis paper provides an overview of techniques developed for the application of support vector regression in the domain of simulation and system identification of helicopter dynamics. A generic high fidelity FLIGHTLAB helicopter model is used to train and validate a number of pitch response SVR models. These models are then trained using flight data from a Sikorsky Seahawk helicopter. The SVR simulation results show significant promise in the ability to represent aspects of a helicopter’s dynamics at a high fidelity. To achieve this, it is important to provide the SVR kernel with knowledge of past inputs that encompass the delay characteristics of the helicopter dynamic system. In this case, the use of nonlinear auto regressive eXogenous input network architecture achieves this goal. Good performance was achieved using input data that encompassed between 300 to 500ms worth of historic response.

scholarly journals A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qinming Liu ◽  
Wenyi Liu ◽  
Jiajian Mei ◽  
Guojin Si ◽  
Tangbin Xia ◽  
...  
Keyword(s):  
Missing Data ◽  
Support Vector Regression ◽  
Prediction Method ◽  
Small Sample ◽  
Support Vector ◽  
Equipment Failure ◽  
Single Variable ◽  
Sample Data ◽  
Data Missing ◽  
Health Diagnosis

Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method.

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection
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