A Hybrid Approach of Fault Inference and Fault Identification for Aircraft Fault Diagnosis

2012 ◽  
pp. 151-158
Author(s):  
Xianhui Liu ◽  
Zhijuan Liu
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Approach ◽  
Fault Identification ◽  
Fault Inference

A hybrid approach for aircraft fault diagnosis based on fault inference and fault identification

2014 ◽  
Vol 118 (1199) ◽  
pp. 81-97 ◽  
Author(s):  
X. Liu ◽  
Z. Liu
Keyword(s):  
Fault Diagnosis ◽  
Flight Control ◽  
Correct Diagnosis ◽  
Hybrid Approach ◽  
Fault Identification ◽  
Multiple Model ◽  
Basic Fault ◽  
Cause Of Failure ◽  
Fault Mode ◽  
Fault Inference

Abstract A cockpit instrumentation system provides various elements of information for pilots. However, logical inference based on a cockpit instruments fault tree (FT) and reliability sometimes cannot give a correct diagnosis of failures. In addition, in flight control systems (FCS), a fault identification method based on the multiple-model (MM) estimator cannot find the basic fault cause. To deal with these problems, a hybrid approach which is capable of integrating inference and fault identification is proposed. In this approach, the event nodes of the FT which have correlations to the FCS are separated into modules. Each module corresponds to a fault mode of the FCS. To use these correlations, fault inference and the MM estimator can share fault diagnosis information. Simulation results show that the proposed approach is helpful in detecting the root cause of failure and is more correct than single fault diagnosis method.

The Design of Remote Monitoring System and Diagnosing System in Natural Gas Compressor

2014 ◽  
Vol 530-531 ◽  
pp. 301-305
Author(s):  
Huan Huan Nie ◽  
Zhen Lu Wu ◽  
Guo Yan Yu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Natural Gas ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Frame Structure ◽  
Inference Rules ◽  
Remote Monitoring System ◽  
Fault Inference ◽  
A Company

The frame structure of monitoring system is introduced and the network structure of monitoring system is analyzed in this paper. Fault diagnosis expert system is put forward and the fault inference rules are elaborated according to the fault of natural gas compressor of a company. The significance of the system is summarized in the end.

Design of Urban-Rural Power Grid Dispatching Fault Diagnosis Expert System

2014 ◽  
Vol 539 ◽  
pp. 659-663 ◽  
Author(s):  
Xiu Feng Xu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Automatic System ◽  
Power Grid ◽  
The Core ◽  
Scada System ◽  
Urban Rural ◽  
Fault Inference ◽  
Fault Information ◽  
Core Part

The paper designs the urban-rural power grid dispatching fault diagnosis expert system which acquires fault information by SCADA system of automatic system of urban-rural power grid, and uses artificial intellegence method to analyze fault information and make fault diagnosis. The paper implements the core part of the fault expert systemthe design of knowledge base and fault inference engine.

Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach

2009 ◽  
Author(s):  
Ehsan Sobhani-Tehrani ◽  
Khashayar Khorasani
Keyword(s):  
Fault Diagnosis ◽  
Nonlinear Systems ◽  
Hybrid Approach

scholarly journals A Complex Fault Diagnostic Approach of Active Distribution Network Based on SBS-SFS Optimized Multi-SVM

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Keyan Liu ◽  
Weijie Dong ◽  
Huanyu Dong ◽  
Jia Wei ◽  
Shiwu Xiao
Keyword(s):  
Fault Diagnosis ◽  
Distribution Networks ◽  
Operating Conditions ◽  
Superior Performance ◽  
Diagnostic Approach ◽  
Fault Identification ◽  
Support Vector ◽  
Distributed Power ◽  
Fault Type ◽  
Fault Diagnostic

After renewable energy distributed generator (DG) is connected to the power grid, traditional diverse-electric-information-based fault diagnosis approaches are not suitable for an active distributed network (ADN) due to the weak characteristics of fault current. Thus, this paper proposes a comprehensive nonformula fault diagnostic approach of ADN using only voltage as input. In the preprocess, sequential forward selection (SFS) and sequential backward selection (SBS) are utilized to optimize the input feature matrix of the sample in order to reduce the information redundancy of multiple measuring points in ADN. Then, a single “1-a-1” support vector machine (SVM) classifier is used for fault identification, and a multi-SVM, with radial basis function (RBF) as the kernel function, is applied to identify the location and fault type. To prove the proposed method is adaptable for ADN, two direct drive fans are used as a DG to test the IEEE 33 node model at every 10% of the line under three operating conditions that include all cases of distributed power generation in ADN. Results comparing real-time and historical data show that the proposed multi-SVM model reaches an average fault type diagnosis accuracy of 97.27%, with a fault identification accuracy of 96%. A backpropagation neural network is then compared to the proposed model. The results show the superior performance of the SBS-SFS optimized multi-SVM. This model can be usefully applied to the fault diagnosis of new energy sources with distributed power access to distribution networks.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

A Research of MM-SVM Technique for Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 2633-2637
Author(s):  
Hong Yin ◽  
Shu Qiang Yang ◽  
Guo Ming Li ◽  
Ping Yin ◽  
Song Chang Jin
Keyword(s):  
Fault Diagnosis ◽  
Satellite Telemetry ◽  
Fault Identification ◽  
Support Vector ◽  
Physical Relationship ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Multiple Support ◽  
Multiple Support Vector Machines ◽  
Satellite Equipment

With the satellite development of our country, higher accuracy and stability are requires, which makes the control systems becoming more complex and requiring more telemetry parameters. Data mining techniques do not consider the physical relationship between the various components, but use of satellite telemetry parameters of the satellite states the purpose of fault identification. In this paper, we give a model based on multiple support vector machines (MM-SVM) technology satellite fault diagnosis method. The experiment shows that our method is effective in satellite equipment fault diagnosis

scholarly journals Intelligent Model-based Integrity Assessment of Nonstationary Mechanical System

2021 ◽  
Author(s):  
Hanxin Chen ◽  
Yuzhuo Miao ◽  
Yongting Chen ◽  
Lu Fang ◽  
Li Zeng ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Particle Filter ◽  
Mechanical System ◽  
Centrifugal Pump ◽  
Bp Neural Network ◽  
Vibration Signal ◽  
Fault Identification ◽  
Integrity Assessment ◽  
Diagnosis Method

The fault diagnosis model for nonstationary mechanical system is proposed in the condition monitoring. The algorithm with an improved particle filter and Back Propagation for intelligent fault identification is developed, which is used to reduce the noise of the experimental vibration signals to delete the negative effect of the noise on the feature extraction of the original vibration signal. The proposed integrated method is applied for the trouble shoot of the impellers inside the centrifugal pump. The principal component analysis (PCA) method optimizes the clean vibration signal to choose the optimal eigenvalue features.The constructed BP neural network is trained to get the condition models for fault identification. The proposed novel model is compared with the BP neural network based on traditional PF and particle swarm optimization particle filter (PSO-PF) algorithm. The BP neural network diagnosis method based on the improved PF algorithm is much better for the integrity assessment of the centrifugal pump impeller. This method is much significant for big data mining in the fault diagnosis method of the complex mechanical system.

scholarly journals Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
Lu Wei ◽  
Zheng Qian ◽  
Yan Pei ◽  
Jingyue Wang
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Euclidean Distance ◽  
Wind Farm ◽  
Hamming Distance ◽  
Fault Identification ◽  
Farm Operators ◽  
One Year ◽  
The Right ◽  
Better Than

Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.

Sign in / Sign up

Export Citation Format

Share Document