Fault Diagnosis Method Based on System-phenomenon-fault Tree

2011 ◽  
Vol 24 (03) ◽  
pp. 466 ◽  
Author(s):  
Guorong CHEN
Keyword(s):  
Fault Diagnosis ◽  
Fault Tree ◽  
Diagnosis Method

Spacecraft Fault Diagnosis Based on Telemetry Data Mining and Fault Tree Analysis and Design of Expert System

2013 ◽  
Vol 760-762 ◽  
pp. 1062-1066 ◽  
Author(s):  
Xiang Gao ◽  
Tao Zhang ◽  
Hong Jin Liu ◽  
Jian Gong
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Telemetry Data ◽  
Tree Analysis ◽  
Analysis And Design ◽  
Diagnosis Method ◽  
The Impact

In this paper, a fault diagnosis method for spacecraft based on telemetry data mining and fault tree analysis was proposed. Decision trees are constructed from the history telemetry data of the spacecraft, and are used to classify the current data which is unknown whether it is fault. If there is a fault, the fault tree method will be used to analyze the fault reason and the impact on the spacecraft system. This method can effectively solve the problem of diagnostic knowledge acquisition. We design and construct a fault diagnosis expert system for spacecraft based on this diagnosis method. An experiment is presented to prove the effectiveness and practicality of the expert system.

Research on fault diagnosis method of rapier loom based on the fusion of expert system and fault tree

2021 ◽  
pp. 1-13
Author(s):  
Yanjun Xiao ◽  
Furong Han ◽  
Yvheng Ding ◽  
Weiling Liu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Fault Tree ◽  
Maintenance Cost ◽  
Intelligent Fault Diagnosis ◽  
Tree Model ◽  
Intelligent Diagnosis ◽  
Diagnosis Method ◽  
System Knowledge

The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms.

scholarly journals Rapid Fault Diagnosis Method of Elevator System Based on Multiattribute Decision Making

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Dapeng Niu ◽  
Chenshu Qi ◽  
Guanghai Li ◽  
Hongru Li ◽  
Hali Pang
Keyword(s):  
Decision Making ◽  
Fault Diagnosis ◽  
Expert Knowledge ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Search Cost ◽  
Diagnosis Method ◽  
Low Efficiency ◽  
Dynamic Attributes ◽  
Location Change

Fault tree analysis is often used in elevator fault diagnosis because of its simplicity and reliability. However, the traditional fault tree method has the problems of low efficiency due to ignoring location change of bottom events during troubleshooting. This paper proposes a rapid diagnosis method based on multiattribute decision making to solve the problem. The fault tree of the elevator system is constructed based on expert knowledge and multisource data, and the location-related matrix is constructed according to the complex vertical structure of the elevator. Then, the attributes of bottom events such as the failure probability, search cost, location time cost, and location-related attributes are comprehensively analyzed in this paper. Finally, the TOPSIS method for dynamic attributes is used based on the work above to achieve the optimal troubleshooting sequence of elevator vibration fault. The results show that the proposed method is more efficient when compared to the optimal troubleshooting sequence obtained by the traditional method.

The Research of Armored Vehicles Weapon System Fault Diagnosis Method

2014 ◽  
Vol 678 ◽  
pp. 309-312
Author(s):  
Hai Feng Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Tree ◽  
Original Data ◽  
Tree Model ◽  
Difficult Situation ◽  
The Neural Network ◽  
Diagnosis Method ◽  
Armored Vehicles ◽  
Neural Network Technology

For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, this paper introduces the fault diagnosis technology based on fault tree model and fault diagnosis based on neural network technology, and the two kinds of fusion technology, complement each other, with a certain type of equipment control system as an example the case analysis, illustrates the fault tree of the neural network and the rationality and validity of the integrated fault diagnosis thinking.

Fault Diagnosis Research of Hydraulic Excavator Based on Fault Tree and Fuzzy Neural Network

2013 ◽  
Vol 303-306 ◽  
pp. 1350-1356
Author(s):  
Guo Ping Li ◽  
Qing Wei Zhang ◽  
Ma Xiao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuzzy Neural Network ◽  
Fault Tree ◽  
Experimental Result ◽  
Hydraulic Excavator ◽  
Tree Model ◽  
Neural Network Learning ◽  
Fuzzy Neural ◽  
Diagnosis Method

Directing to the dispersiveness and faintness failure characteristics of hydraulic excavator, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device.

Fault Diagnosis Mechanism Based on FTA and Bayesian for Large-Scale CNC Equipments

2012 ◽  
Vol 236-237 ◽  
pp. 474-479
Author(s):  
Jin Fei Liu ◽  
Ming Chen ◽  
Ying Lei Li
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Posterior Probability ◽  
Bayesian Method ◽  
High Efficiency ◽  
Fault Tree ◽  
Nc Machine ◽  
Diagnosis Method ◽  
Cut Set ◽  
Fault Diagnosis System

In view of the disadvantage of current FAT-based fault diagnosis method in large-scale complicated system, fault diagnosis method of heavy NC machine based on FTA and Bayesian is discussed. Firstly, building fault trees with the help of reachability matrix, and to set the determinate conditions at every node of fault tree combining FTA with rule reasoning, the minimum cut set of fault reasons are determined as a result of step by step screening fault tree from top-down; Secondly, Bayesian method is integrated into the fault tree diagnostic method to calculate the posterior probability triggered by each fault tree in order to locate the fault tree where the fault had occurred and ensure high efficiency of fault diagnosis; Finally, B/S based intelligent fault diagnosis system for large-scale CNC equipments is developed, and the feasibility and efficiency of this method are proved in an example of fault diagnosis of Φ 160 NC boring and milling machine.

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