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.

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.

Bayesian Diagnostic Network Model for Sliding Plug Door

2011 ◽  
Vol 219-220 ◽  
pp. 1496-1499 ◽  
Author(s):  
Hui Chao Shi ◽  
Long Tian ◽  
Liang Wang
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Expert System ◽  
Bayesian Network ◽  
Knowledge Base ◽  
Network Model ◽  
Model Building ◽  
Bayesian Network Model ◽  
Structure Tree ◽  
System Knowledge

For constructing Bayesian diagnostic network model of complex system is a difficult course, we propose a Bayesian network model auto-construction method based on expert system knowledge base. Bayesian diagnostic network model was built by using the CM structure, and the diagnostic knowledge was organized by product structure tree. We have applied this method to fault diagnosis for sliding plug door, and tested our methodology on many examples of diagnostic problems of sliding plug door, which prove the efficiency of the Bayesian diagnostic network model and model-building 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.

Establishment of Aircraft Ammunition Fault Diagnosis Expert System Knowledge Base Based on FTA

2011 ◽  
Vol 121-126 ◽  
pp. 3909-3913 ◽  
Author(s):  
Xin Hui Zhang ◽  
Shi Liang Yang ◽  
Wei Kui Wang ◽  
Yun Liu ◽  
Yan Wei
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
System Knowledge ◽  
Normal Storage

Aircraft ammunitions inevitably suffer various faults during its storage, use and maintenance. The faults may result in disastrous consequences or make combat missions fail to meet related tactical and strategic requirements, so their diagnoses are significant for the safe and normal storage and use in combats. The diagnoses of the faults are complicated and difficult and always need the participation of experts who are small in quantity. Thus, this paper studies and develops the establishment of an aircraft ammunition fault diagnosis expert system knowledge base for the purposes of better collection, summarization and promotion of the precious experience of experts and a higher fault diagnosis level.

Sign in / Sign up

Export Citation Format

Share Document