Test strategy optimization method for aircraft system fault diagnosis

2014 ◽  
Author(s):  
Yan Su ◽  
Rong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Optimization Method ◽  
Test Strategy ◽  
Aircraft System

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

scholarly journals Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042005
Author(s):  
Xueyi Liu ◽  
Junhao Dong ◽  
Guangyu Tu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Performance Prediction ◽  
Optimization Method ◽  
Aerodynamic Performance ◽  
Evaluation Index ◽  
Genetic Optimization ◽  
Quantum Genetic Algorithm

Abstract Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

A general trajectory optimization method for aircraft taxiing on flight deck of carrier

2018 ◽  
Vol 233 (4) ◽  
pp. 1340-1353 ◽  
Author(s):  
Yu Wu ◽  
Ning Hu ◽  
Xiangju Qu
Keyword(s):  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Fitness Function ◽  
Control Variable ◽  
Optimization Method ◽  
Operation Efficiency ◽  
Ground Motion Model ◽  
Aircraft System ◽  
Chicken Swarm Optimization ◽  
Flight Deck

Enhancing operation efficiency of flight deck has become a hotspot because it has an important impact on the fighting capacity of the carrier–aircraft system. To improve the operation efficiency, aircraft need taxi to the destination on deck with the optimal trajectory. In this paper, a general method is proposed to solve the trajectory optimization problem for aircraft taxiing on flight deck considering that the existing methods can only deal with the problem in some specific cases. Firstly, the ground motion model of aircraft, the collision detection strategy and the constraints are included in the mathematical model. Then the principles of the chicken swarm optimization algorithm and the generality of the proposed method are explained. In the trajectory optimization algorithm, several strategies, i.e. generation of collocation points, transformation of control variable, and setting of segmented fitness function, are developed to meet the terminal constraints easier and make the search efficient. Three groups of experiments with different environments are conducted. Aircraft with different initial states can reach the targets with the minimum taxiing time, and the taxiing trajectories meet all the constraints. The reason why the general trajectory optimization method is validated in all kinds of situations is also explained.

MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method

2020 ◽  
Vol 34 (14) ◽  
pp. 2059048
Author(s):  
Tong Gao ◽  
Wei Sheng ◽  
Mingliang Zhou ◽  
Bin Fang ◽  
Liping Zheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Optimization Method ◽  
Learning Rate ◽  
Data Driven ◽  
Adaptive Learning Rate ◽  
Improved Performance ◽  
Rate Optimization

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

Support Vector Machine Optimization Based on Bacterial Foraging Algorithm and Applied in Fault Diagnosis

2011 ◽  
Vol 216 ◽  
pp. 153-157
Author(s):  
D.L. Yang ◽  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Rolling Bearing ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

The parameter optimization is the key to study of support vector machine (SVM). With strong global search capability of bacterial foraging algorithm(BFA), the optimization method—support vector machine parameters optimization based on bacterial foraging algorithm was proposed, which can achieve the dynamic optimization of the parametersCandγ,and overcomes the problem of inefficiency for selecting reasonable parameters according to the experience in the traditional fault diagnosis. Compared with other methods, the BFA is simpler and easier for programming, and the optimization SVM model become smaller. The rolling bearing fault diagnosis results show that bacterial foraging algorithm is suitable for support vector machine parameter optimization.

Research on Modeling Method Based on Multi-Signal Flow Model

2014 ◽  
Vol 494-495 ◽  
pp. 983-988
Author(s):  
Zhi Qiang Wang ◽  
Xu Yan ◽  
Chun Ping Wang
Keyword(s):  
Fault Diagnosis ◽  
Flow Model ◽  
Optimization Method ◽  
Test System ◽  
Test Point ◽  
Modeling Method ◽  
System Model ◽  
State Monitoring ◽  
Signal Flow ◽  
Test Points

Establishing a test system model is necessary guarantee to realize the state monitoring, and also the premise of optimizing test points. The modeling method, principle and steps based on multi-signal flow model were analyzed in this paper. And a test point optimization method based on this model was studied. Through analysis and comparison, the proposed method can reduce complexity of monitoring system, avoid blindness of the diagnosis and reasoning process, and improve efficiency of fault diagnosis.

Simulation for Automotive Engine Fault Diagnosis Method

2014 ◽  
Vol 687-691 ◽  
pp. 882-885
Author(s):  
Huan Xue Liu ◽  
Guang Dong Zhang ◽  
Zhen Zhong Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Accurate Data ◽  
Swarm Optimization ◽  
Automotive Engine ◽  
Diagnosis Method

For engine fault diagnosis problem, an engine fault diagnosis method based on particle swarm optimization algorithm is proposed. The velocity and spatial position of all the particles in the particle swarm are updated, in order to provide accurate data basis for the engine fault diagnosis. Particle swarm optimization method is utilized to process iteration for all particles, so as to determine whether failure exists in components of engine. Experimental results show that with the proposed algorithm to diagnose engine fault can effectively improve the accuracy of fault diagnosis, and achieved the desired results.

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