Fault Diagnosis Technology Research on Radar Equipment Based on Sensor Data Validation and Detection Fusion

2013 ◽  
Vol 347-350 ◽  
pp. 387-391
Author(s):  
Wen Hua Hu ◽  
Bao Feng Guo ◽  
Dong Fang Xue
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic System ◽  
Sensor Data ◽  
Data Validation ◽  
Characteristic Parameters ◽  
Proposed Model ◽  
Diagnosis Rate ◽  
Diagnosis Method ◽  
Fault Diagnosis System ◽  
Fault Characteristic

Aiming at many fault characteristic parameters of modern radar and the problem that general diagnosis method is difficult to locate fault accurately. The dynamic mathematical model of radar equipment fault diagnosis system is built. The uncertainty theoretic analysis of fault diagnosis result is studied from two aspects, data processing channel and fault model. This paper combines sensor data validation with detection fusion technology, and then puts forward the fault diagnosis technology based on sensor data validation and detection fusion. The structure of the diagnostic system model is given. Case analysis proves that the proposed model and method has a significant role in improving detection performance, reducing the Bayes risk and improve fault diagnosis rate.

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 Fault Diagnosis of Brake Train Based on Multi-Sensor Data Fusion

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4370
Author(s):  
Yongze Jin ◽  
Guo Xie ◽  
Yankai Li ◽  
Xiaohui Zhang ◽  
Ning Han ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expectation Maximization ◽  
Monitoring Data ◽  
Sensor Data ◽  
Operating Environment ◽  
Mass Model ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Mass ◽  
Parameter Identifications

In this paper, a fault diagnosis method is proposed based on multi-sensor fusion information for a single fault and composite fault of train braking systems. Firstly, the single mass model of the train brake is established based on operating environment. Then, the pre-allocation and linear-weighted summation criterion are proposed to fuse the monitoring data. Finally, based on the improved expectation maximization, the braking modes and braking parameters are identified, and the braking faults are diagnosed in real time. The simulation results show that the braking parameters of systems can be effectively identified, and the braking faults can be diagnosed accurately based on the identification results. Even if the monitoring data are missing or abnormal, compared with the maximum fusion, the accuracies of parameter identifications and fault diagnoses can still meet the needs of the actual systems, and the effectiveness and robustness of the method can be verified.

Remote Fault Diagnosis System of Ultrahigh Speed Grinding Based on Multi-Agent

2009 ◽  
Vol 76-78 ◽  
pp. 67-71
Author(s):  
Wan Shan Wang ◽  
Tian Biao Yu
Keyword(s):  
Fault Diagnosis ◽  
Theoretical Research ◽  
Case Based Reasoning ◽  
Diagnosis System ◽  
Knowledge Reasoning ◽  
Diagnosis Method ◽  
Multi Agent ◽  
Fault Diagnosis System ◽  
Set Up ◽  
Remote Fault Diagnosis

A remote fault diagnosis method for ultrahigh speeding grinding based on multi-agent is presented. The general faults of ultrahigh speed grinding are analyzed and diagnosis model based on multi-agent is established, the dialogue layer, problem decomposition layer, control layer and problem solving layer in the process of diagnosis are studied and the knowledge reasoning model of fault diagnosis is set up based case-based reasoning (CBR) combining rule-based reasoning (RBR). Based on theoretical research, a remote fault diagnosis system of ultrahigh speed grinding is developed. Results of the system running prove the theory is correctness and the technology is feasibility.

Application of Integrated Neural Network Information Fusion in Fault Diagnosis of one Missile Launching Control Unit

2013 ◽  
Vol 427-429 ◽  
pp. 2808-2812
Author(s):  
Xu De Cheng ◽  
Hong Li Wang ◽  
Bing Xu ◽  
Xue Dong Xue
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Preliminary Analysis ◽  
Control Unit ◽  
Fusion Technology ◽  
Network Information ◽  
Diagnosis Rate ◽  
Fault Diagnosis System ◽  
Made In

Research and development of fault diagnosis system in application of integrated neural network information fusion is based on information fusion technology, with which preliminary analysis of equipment fault is made in different perspectives in terms of neural network, so as to identify the fault on the basis of fusion outcome. This technique is applied in fault diagnosis of one type of missile launching control unit, leading to sufficient use of various information and substantially increased fault diagnosis rate.

Fault Diagnosis System Based on Fuzzy Theory of Vehicle Air Conditioner

2013 ◽  
Vol 329 ◽  
pp. 324-328
Author(s):  
Ying Hui Wang ◽  
Shu Sheng Xiong ◽  
Wen Lang ◽  
Yi Tian Tang
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Theory ◽  
Air Conditioner ◽  
Diagnosis System ◽  
Diagnosis Method ◽  
Fault Diagnosis System

To maintain the vehicle air conditioner efficiently, a fault diagnosis system based on fuzzy theory is prospected in this paper. A fault diagnosis method based on fuzzy theory was given. And according to the method, a fault diagnosis program was written with labview. Experiments proved that the fault diagnosis program was stable and functional. The accuracy of this fault diagnosis system is more than 80%. The system can be used to diagnosed the malfunction of vehicle air conditioner efficiently and discover the potential fault in time, helping to eliminate hidden dangers.

Engine Fault Diagnostic System Based on Non-Linear Support Vector Machine

2012 ◽  
Vol 229-231 ◽  
pp. 534-537
Author(s):  
Gao Huan Xu ◽  
Jun Xiang Ye
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Diagnostic System ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Diagnosis System ◽  
The Core ◽  
Wavelet Energy ◽  
Non Linear ◽  
Fault Diagnosis System

The car engine failures in the course of time and place have many possibilities. The engine fault diagnosis system developed in .NET platform. The core of the system make use of noise wavelet energy features and non-linear support vector machine classification. After the experiment, the system has fairly good results.

Research on Equipment Fault Diagnosis Method Based on Multi-Sensor Data Fusion

2012 ◽  
Vol 466-467 ◽  
pp. 1222-1226
Author(s):  
Bin Ma ◽  
Lin Chong Hao ◽  
Wan Jiang Zhang ◽  
Jing Dai ◽  
Zhong Hua Han
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Diagnostic Accuracy ◽  
Evidence Theory ◽  
Experimental Results ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Diagnosis Method ◽  
Single Sensor

In this paper, we presented an equipment fault diagnosis method based on multi-sensor data fusion, in order to solve the problems such as uncertainty, imprecision and low reliability caused by using a single sensor to diagnose the equipment faults. We used a variety of sensors to collect the data for diagnosed objects and fused the data by using D-S evidence theory, according to the change of confidence and uncertainty, diagnosed whether the faults happened. Experimental results show that, the D-S evidence theory algorithm can reduce the uncertainty of the results of fault diagnosis, improved diagnostic accuracy and reliability, and compared with the fault diagnosis using a single sensor, this method has a better effect.

Fuzzy Diagnosis of Tapping Process Fault

2009 ◽  
Vol 626-627 ◽  
pp. 207-212 ◽  
Author(s):  
H.L. Xue ◽  
Le Wang ◽  
Xue Yong Chen ◽  
Gui Cheng Wang
Keyword(s):  
Experimental Study ◽  
Fault Diagnosis ◽  
Fuzzy Theory ◽  
Weight Matrix ◽  
Diagnosis System ◽  
Process Failure ◽  
Diagnosis Method ◽  
Fault Diagnosis System ◽  
Fuzzy Diagnosis ◽  
Types Of Faults

Based on analyzing the problem of tap worn and broken in the tapping process, the faults in tapping process are classified into four types: tap worn, chips jamming, uneven hardness of material and the tapping process failure; According to the fuzzy theory, this paper describes the torque characteristic of the four types of faults, ascertains the characteristic vector of fault, presents the weight matrix among faults, puts forward the judgment method of system faults and establishes the fuzzy fault diagnosis system in tapping process. The experimental study shows that the fuzzy diagnosis method can effectively identify the four types of faults in tapping process and guard against tap broken.

Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

2011 ◽  
Vol 219-220 ◽  
pp. 1077-1080
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Effective Combination ◽  
Fault Diagnosis System ◽  
Network Fault ◽  
Fault Characteristic

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks

2000 ◽  
Author(s):  
Pong-Jeu Lu ◽  
Ming-Chuan Zhang ◽  
Tzu-Cheng Hsu ◽  
Jin Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Success Rate ◽  
Neural Nets ◽  
Sensor Data ◽  
Trend Detection ◽  
Influence Coefficient ◽  
Data Validation ◽  
Engine Operation ◽  
Artificial Neural

Application of artificial neural network (ANN)-based method to perform engine condition monitoring and fault diagnosis is evaluated. Back-propagation, feedforward neural nets are employed for constructing engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that under high-level noise conditions ANN fault diagnosis can only achieve a 50–60% success rate. For situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both 4-input and 8-input ANN diagnoses achieve high scores which satisfy the minimum 90% requirement. It is surprising to find that the success rate of the 4-input diagnosis is almost as good as that of the 8-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, it is found that a preprocessor that can perform sensor data validation is of paramount importance. Auto-associative neural network (AANN) is introduced to reduce the noise level contained. It is shown that the noise can be greatly filtered to result in a higher success rate of diagnosis. This AANN data validation preprocessor can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection. It is concluded that ANN-based fault diagnostic method is of great potential for future use. However, further investigations using actual engine data have to be done to validate the present findings.

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