Simultaneous Fault Diagnosis of the Reactor Coolant System Based on the DSM Evidence Theory

2013 ◽  
Author(s):  
Jinshen Ren ◽  
Botao Jiang ◽  
Fuyu Zhao
Keyword(s):  
Fault Diagnosis ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Information Uncertainty ◽  
Test Bed ◽  
Reactor Coolant ◽  
Wide Range ◽  
Diagnosis Method ◽  
Sensor Information ◽  
Coolant System

Fault diagnosis of nuclear power plant (NPP) has become a major research topic in ensuring the reliability and safety for the NPP operation. In the light of complexity and information uncertainty of nuclear reactor coolant system, some diagnosis techniques based on the multi-sensor information fusion theories have been widely used. As one part of the those theories, the Dempster-Shafer (D-S) theory excelled in demonstrating and combining uncertain information has witnessed a wide range of applications in the fault diagnosis fields. It can be inferred from the previous studies that the fault diagnosis method based on the D-S theory is efficient; However, this method is mainly intended for single fault diagnosis. In fact, in practical application, simultaneous faults often occur. Based on the DSm theory (Dezert-Smarandache theory of plausible and paradoxical reasoning), this paper proposes a simultaneous fault diagnosis method, which is intended to deal with the simultaneous faults in the reactor coolant system and adequately cope with the sensor information uncertainty. This method can be divided into four steps. Firstly, with the features of the simultaneous fault considered, a diagnosis model of the simultaneous fault is designed, which is based on the DSm theory. Secondly, for the instant information of each senor, the fuzzy membership degree is used to set the corresponding basic probability assignment (BPA) function for each sensor. Thirdly, the above-mentioned BPA functions are combined by utilizing the classic DSm rules. Finally, a decision on the basis of maximum support rule and absolute support rule is made to determine the real fault modes. After that, the experiments on the test-bed are conducted to prove the efficiency of this method.

scholarly journals Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Naiquang Su ◽  
Xiao Li ◽  
Qinghua Zhang ◽  
Zhiqiang Huo ◽  
Xavier Chiementin
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Large Scale ◽  
Evidence Theory ◽  
Rotating Machinery ◽  
Uncertain Information ◽  
Sample Distribution ◽  
Multiple Data ◽  
Diagnosis Method ◽  
Conflict Information

Due to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults diagnosis method for rotating machinery of the large unit based on evidence theory and multi-information fusion. The evidence theory and multi-information fusion method mainly deal with multisource information and conflict information, synthesize multiple uncertain information, and obtain synthetic information from multiple data sources. To detect faults in rotating machinery, the dimensionless index ranges of composite faults are first used to form a feature set as the reference. Then, a two-sample distribution test is applied to compare the known fault samples with the tested fault samples, and the maximum statistical distance is used. Finally, the multiple maximum statistical distances are fused by evidence theory and identifying fault types based on the fusion result. The proposed method was applied to the large petrochemical unit simulation experiment system, the results of which showed that our proposed method could accurately identify composite faults and provide maintenance guidance for composite fault diagnosis.

Fault Diagnosis for the Electric Starting System of Self-Propelled Artillery Based on Information Fusion Technology

2014 ◽  
Vol 940 ◽  
pp. 280-283
Author(s):  
Chong Fa Liu ◽  
Zheng Xi Xie ◽  
Jie Min Yang ◽  
Zhi Jun Gao
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
System Level ◽  
Diagnostic Process ◽  
Comprehensive Treatment ◽  
Information Uncertainty ◽  
Fusion Technology ◽  
Source Information ◽  
Sensor Information

Fault diagnosis based on multi-sensor information fusion technology processes multi-source information and data of the monitoring system in various manners such as detection, parallel and related processing, estimation, comprehensive treatment and so on so as to maximize the use of system knowledge and the information provided by the available detectable quantity of the system in fault diagnosis. Compared with the single sensor, multi-sensor information fusion enjoys obvious advantages in reducing information uncertainty, improving information accuracy obtained by the system and advancing system reliability and fault tolerance capability. As the accuracy of traditional fault diagnosis method is not high, considering the characteristics of faults in the electric starting system of self-propelled gun, a method of fault diagnosis is presented here based on network information fusion technology. The diagnostic process is divided into two level diagnosis, that is subsystem and system level. System adopts BP neural network in fault mode classification, while at system level D-S evidence theory is used in the process of synthetic decision evaluation on the entire system malfunction, ensuring accurate and fast fault diagnosis, which greatly shorten the corrective maintenance time.

scholarly journals A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 4017 ◽  
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Zitao Zheng ◽  
Bing Qi ◽  
Liwei Zhang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Evidence Theory ◽  
Gas Analysis ◽  
Power Transformers ◽  
Support Vector ◽  
Dissolved Gas ◽  
Dissolved Gas Analysis ◽  
Diagnosis Method ◽  
Multiclass Support Vector Machine

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster–Shafer (D–S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D–S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

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.

scholarly journals A Power Grid Fault Diagnosis Method in the Case of Alarm Information Loss Based on the Top-k Skyline Query Algorithm

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6223
Author(s):  
Lei Jin ◽  
Qing Chen ◽  
Jinjie Ji ◽  
Xiaotong Zhou
Keyword(s):  
Fault Diagnosis ◽  
Power Grid ◽  
Circuit Breaker ◽  
Information Loss ◽  
Uncertain Information ◽  
Skyline Query ◽  
Data Set ◽  
Information Set ◽  
Diagnosis Method ◽  
Query Algorithm

After the failure of the power system, a large amount of alarm information will flood into the dispatching terminal instantly. At the same time, there are inevitable problems, such as the abnormal operation of the protection and the circuit breaker, the lack of alarm information, and so on. This kind of uncertainty problem brings great trouble to the fault diagnosis algorithm. As a data processing algorithm for an uncertain information set, Top-k Skyline query algorithm can eliminate the data points that do not meet the requirements in the information set, and then output the final K results in order. Based on this background, this paper proposes a power grid fault diagnosis method based on the Top-k Skyline query algorithm considering alarm information loss. Firstly, the fault area is determined by using the information of the electrical quantity and switching value. Then, backward reasoning Petri nets are established for the nodes in the fault area to form the data set of fault hypotheses. Then, the Top-k Skyline query algorithm is used to sort the hypotheses and choose the hypothesis with higher reliability. Finally, an IEEE 39-bus system example is given to verify the reliability of the proposed method.

scholarly journals Power Grid Fault Diagnosis Method Using Intuitionistic Fuzzy Petri Nets Based on Time Series Matching

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Mingyue Tan ◽  
Jiming Li ◽  
Xiangqian Chen ◽  
Xuezhen Cheng
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Petri Nets ◽  
Power Grid ◽  
New Method ◽  
Uncertain Information ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Petri Nets ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the reliability of power grid fault diagnosis by enhancing the processing ability of uncertain information and adequately utilizing the alarm information about power grids, a fault diagnosis method using intuitionistic fuzzy Petri Nets based on time series matching is proposed in this paper. First, the alarm hypothesis sequence and the real alarm sequence are constructed using the alarm information and the general grid protection configuration model, and the similarity of the two sequences is used to calculate the timing confidence. Then, an intuitionistic fuzzy Petri Nets fault diagnosis model, with an excellent ability to process uncertain information from intuitionistic fuzzy sets, is constructed, and the initial place value of the model is corrected by the timing confidence. Finally, an application of the fault diagnosis model for the actual grid is established to analyze and verify the diagnostic results of the new method. The results for some test cases show that the new method can improve the accuracy and fault tolerance of fault diagnosis, and, furthermore, the abnormal state of the component can be inferred.

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