The Faults Detection Methods for Embedded Equipment

2014 ◽  
Vol 602-605 ◽  
pp. 2041-2043
Author(s):  
Bin Wang
Keyword(s):  
Fault Diagnosis ◽  
Failure Modes ◽  
Simulation Models ◽  
Classification Model ◽  
Detection Methods ◽  
Control Procedure ◽  
Element Analysis ◽  
Embedded Control ◽  
Diagnosis Method ◽  
Simulation Results

Due to the objects in the embedded control procedure are difficult to obtain a variety of fault data and fault features, it’s necessary to establish simulation models in accordance with the operational mechanisms of the embedded equipment to simulate and diagnose the practical faults. This paper proposes a SVM integrated diagnostic method and further proposes the faults classification model with improved neural network. The faults diagnose performance is greatly improved by analyzing the types of the faults in different facets. For the embedded valve failure modes, the simulation results of the proposed method are compared with that of the previous mature independent element analysis method. The simulation results show that the fault diagnosis method in this paper can effectively improve the speed and accuracy of fault diagnosis for the embedded equipment.

Effect of Strain Rate on the Simulation of MEMS Safety and Arming Device

2014 ◽  
Vol 609-610 ◽  
pp. 849-855
Author(s):  
Wen Rui Ma ◽  
Guang He
Keyword(s):  
Finite Element Analysis ◽  
Strain Rate ◽  
Impact Load ◽  
Simulation Models ◽  
Strain Rate Effect ◽  
Rate Effect ◽  
Experimental Results ◽  
Analysis Software ◽  
Element Analysis ◽  
Simulation Results

Under launch impact load, LIGA nickel that manufacturing MEMS fuze safety and arming (S&A) device will have obvious strain rate effect. By using finite element analysis software ANSYS/LS-DYNA, simulation models of a small-caliber ammunition MEMS fuze setback S&A device with strain rate effect and without strain rate effect were respectively established. The results of the two simulation modules were quite different. Comparisons between experimental results and simulation results show that simulation results considering strain rate effect agree well with experimental results, which proves strain rate effect should not be ignored in the simulation of MEMS S&A device.

scholarly journals Multi-Fault Diagnosis Approach Based on Updated Interacting Multiple Model for Aviation Hydraulic Actuator

Information ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 410
Author(s):  
Xiaozhe Sun ◽  
Xingjian Wang ◽  
Siru Lin
Keyword(s):  
Fault Diagnosis ◽  
Flight Control ◽  
Failure Modes ◽  
Physical Parameters ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Hydraulic Actuator ◽  
Multiple Faults ◽  
Actuation System ◽  
Diagnosis Method

The aviation hydraulic actuator (HA) is a key component of the flight control system in an aircraft. It is necessary to consider the occurrence of multiple faults under harsh conditions during a flight. This study designs a multi-fault diagnosis method based on the updated interacting multiple model (UIMM). The correspondence between the failure modes and the key physical parameters of HA is found by analyzing the fault mode and mechanism. The key physical parameters of HA can be estimated by employing a series of extended Kalman filters (EKF) related to the different modes of HA. The models in UIMM are updated once the fault is determined. UIMM can reduce the number of fault models and avoid combinatorial explosion in the case of multiple faults. Simulation results indicate that the multi-fault diagnosis method based on UIMM is effective for multi-fault diagnosis of electro-hydraulic servo actuation system.

Fault Diagnosis of Traction Inverter Based on Wavelet Analysis and Neural Network

2014 ◽  
Vol 1061-1062 ◽  
pp. 1025-1030
Author(s):  
Ya Fei Wang ◽  
Wen Ming Zhang ◽  
Xing Lai Ge ◽  
Yang Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Open Circuit ◽  
Phase Current ◽  
Three Phase ◽  
Simulation Results

Due to IGBT open-circuit fault of CRH2 EMU’s traction inverter, a method of its fault diagnosis with the three-phase current signals as detection objects is conducted. By applying the wavelet analysis, three-phase current signals are decomposed for four times. With the coefficients of each layer obtained, the energy values of layers are calculated as well as the vectors corresponding to failure modes. According to the vectors regarded as input and the expected output, a BP neural network is established. Through training the network, the parameters of network can be defined. In addition, to test and evaluate the performance of network, certain noise is added to the three-phase current signals. Simulation results show it is feasible for the fault diagnosis of traction inverter.

Study on Automated Manual Transmission Fault Diagnosis and Tolerant Control

2014 ◽  
Vol 697 ◽  
pp. 344-349
Author(s):  
Jian Bo Yin ◽  
Qi Chen ◽  
Yun Bo Ma ◽  
Yv Cai Zhao
Keyword(s):  
Mathematical Model ◽  
Fault Diagnosis ◽  
Manual Transmission ◽  
Sensors And Actuators ◽  
Information Redundancy ◽  
Matlab Simulink ◽  
Diagnosis Method ◽  
Automated Manual Transmission ◽  
Simulation Results ◽  
Engine Clutch

This paper discuss the possible fault about AMT. By using the information redundancy between those parts, the faults of sensors and actuators can be found. In addition, the corresponding tolerant is put forward. Based on the fault diagnosis method, Matlab/Simulink mathematical model of engine, clutch and transmission is built. The simulation results show that the model can satisfy the requirement of fault diagnosis, and has certain tolerances.

Intelligent Fault Diagnosis Method Based on Vector-Bispectrum and SVDD

2012 ◽  
Vol 490-495 ◽  
pp. 1029-1033 ◽  
Author(s):  
Ling Jun Li ◽  
Wen Ping Lei ◽  
Jie Han ◽  
Wang Shen Hao
Keyword(s):  
Fault Diagnosis ◽  
Field Vector ◽  
Classification Model ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Nonlinear Properties ◽  
Channel Information ◽  
Diagnosis Method ◽  
Set Up

Support vector data description (SVDD) can be used to solve the problems of the insufficient fault samples in the fault diagnosis field. Vector-bispectrum is the bispectrum analysis method based on the full vector spectrum information fusion. It can be used to fuse the double-channel information of the rotary machines effectively and reflect the nonlinear properties in the signals more completely and accurately. In order to realize the aim that the faults of the machines can be diagnosed effectually and intelligently under the situation of the lack of the fault samples, the intelligent diagnosis method of the faults by combining the vector-bispectrum with SVDD is put forward. By using the vector-bispectrum to process the signals and extract the characteristic vectors, which can be used as the input parameters of SVDD. The classification model is set up and therefore the running states of the machines can also be classified. The method is applied to the gearbox fault diagnosis. The results indicate that the method can be effectively used to extract the characteristic information of the gearbox signals and increase the accuracy of SVDD in the fault diagnosis.

Sensor Fault Diagnosis Based on SOFM Neural Network

2014 ◽  
Vol 511-512 ◽  
pp. 193-196
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
High Accuracy ◽  
Classification Methods ◽  
Statistical Classification ◽  
Sensor Fault ◽  
Discriminant Functions ◽  
Fast Speed ◽  
Diagnosis Method ◽  
Simulation Results

Traditional sensor fault diagnosis is mainly based on statistical classification methods. The discriminant functions in these methods are extremely complex, and typical samples of reference modes are not easy to get, therefore it is difficult to meet the actual requirements of a project. In view of the deficiencies of conventional sensor fault diagnosis technologies, a fault diagnosis method based on self-organizing feature map (SOFM) neural network is presented in this paper. And it is applied to the fault diagnosis of pipeline flow sensor in a dynamic system. The simulation results show that the fault diagnosis method based on SOFM neural network has a fast speed, high accuracy and strong generalization ability, which verifies the practicality and effectiveness of the proposed method.

Fault Diagnosis of Broken Bars in Induction Motors Using EMD

2013 ◽  
Vol 373-375 ◽  
pp. 861-864
Author(s):  
Xiao Quan Li ◽  
Jing Chen Wang ◽  
Run Ling Li
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
High Sensitivity ◽  
Fundamental Wave ◽  
Stator Current ◽  
Analogue Signal ◽  
Characteristic Value ◽  
Diagnosis Method ◽  
Simulation Results ◽  
Fault Characteristic

This paper presents a diagnosis method based on EMD, in order to improve the precision of fault diagnosis in induction motors. After the Park vector analogue signal of stator current is decomposed by EMD, 2sf fault characteristic value is abstracted accurately. Simulation results show that this proposed method is of high sensitivity, efficiently solves the problem that frequencies of fault and fundamental wave are close and difficult to be decomposed.

Using ASTFT Spectrum to Suppress Cross Terms in WVD and its Application in Fault Diagnosis

2009 ◽  
Vol 626-627 ◽  
pp. 535-540
Author(s):  
B.P. Tang ◽  
F. Li ◽  
W.Y. Liu
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Effective Suppression ◽  
Cross Terms ◽  
Diagnosis Method ◽  
Simulation Results ◽  
Short Time

A new fault diagnosis method to suppress cross terms of Wigner-Ville distribution (WVD) using Adaptive Short-time Fourier Transform (ASTFT) spectrum is put forward. The relationships of correlation between auto terms and cross terms of WVD are obtained theoretically by analyzing the WVD. Firstly, the signal ASTFT spectrum which can determine the signal component positions in the time-frequency plane is obtained. Then, the ASTFT spectrum as a window function is selected to process the signal WVD. Thus the cross terms can be effectively restrained. The simulation results show that a better resolution and more effective suppression of cross terms can be obtained. At last, the proposed method is applied to the fault diagnosis of bearing. The simulation and the experiment results indicate that the proposed method is effective in feature extraction.

scholarly journals A method for bearing fault diagnosis of mine hoist using convolutional attention autoencoder

2021 ◽  
Author(s):  
Jie Zhang ◽  
Ke Yang ◽  
Yuanyuan Jiang ◽  
Ling Xia
Keyword(s):  
Fault Diagnosis ◽  
Input Signal ◽  
Experimental Validation ◽  
Failure Modes ◽  
Noisy Environment ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Diagnosis Method ◽  
Actual Operation ◽  
Mine Hoist

Abstract In view of the complex environment and frequent faults in the actual operation of mine hoist, a fault diagnosis method based on Convolution Attention Autoencoder (CAAE) is proposed through theoretical analysis and experimental verification to improve the diagnostic stability of mine hoist under strong noise. First, a CAAE is constructed, which uses a combination of a convolutional neural network (CNN) and a channel attention module (CAM) to compress and encode the input signal, and then the input signal is reconstructed by a decoder to train the CAAE to extract the original signal fault features. Then, a fault diagnosis classifier is constructed to classify different fault patterns. Finally, experimental validation is performed with the Case Western Reserve University bearing dataset. The results show that the method has a strong feature extraction capability and a high classification accuracy for bearing failure modes compared with existing methods. And the experiments on the application effect of the proposed method in noisy environment are conducted to verify that the method is highly effective and challenging.

Fault Diagnosis of Analog Circuits Based on Evidence Theory

2014 ◽  
Vol 556-562 ◽  
pp. 2149-2152
Author(s):  
Cheng Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Analog Circuits ◽  
Bp Neural Network ◽  
Analog Circuit ◽  
Evidence Theory ◽  
Fusion Technology ◽  
Diagnosis Method ◽  
Simulation Results

BP neural network and evidence theory data fusion technology can be used in troubleshooting electronic equipment, from the simulation results show that the fault diagnosis method based on evidence theory and BP neural network can effectively diagnose faults in analog circuit, and it has automated intelligent characteristics.

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