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.

Fault Diagnosis System for Large-Scale Equipments Based on Hybrid Reasoning

2011 ◽  
Vol 201-203 ◽  
pp. 956-961
Author(s):  
Ming Chen ◽  
Rui Zhang ◽  
Ying Lei Li
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Cross Correlation ◽  
Fault Tree ◽  
Case Based Reasoning ◽  
Complex Structures ◽  
Diagnosis System ◽  
Trouble Shooting ◽  
Hybrid Reasoning ◽  
Fault Diagnosis System

Because of their complex structures, diverse functions, and cross-correlation among subsystems, the fault of large-scale equipments occurs easily, but its trouble shooting is difficult. Firstly, a hybrid reasoning method is proposed, and the framework of fault diagnosis system is constructed according to characteristics of case based reasoning (CBR) and rule based reasoning (RBR). Secondly, CBR and RBR applied to fault diagnosis for large-scale NC equipments are analyzed. In RBR process, the fault tree was obtained by reachability matrix, and the rules knowledge is automatically generated by fault tree, so the bottleneck of acquiring rules knowledge is solved. Lastly, this method is used in the fault diagnosis of certain large-scale NC equipment, which verifies the validity of the method.

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.

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.

A Fault Diagnosis Method Based on Constrained Frequent Pattern Trees

2010 ◽  
Vol 39 ◽  
pp. 449-454
Author(s):  
Jiang Hui Cai ◽  
Wen Jun Meng ◽  
Zhi Mei Chen
Keyword(s):  
Fault Diagnosis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Predicate Logic ◽  
Frequent Pattern ◽  
Rule Mining ◽  
First Order ◽  
Broad Term ◽  
Diagnosis Method

Data mining is a broad term used to describe various methods for discovering patterns in data. A kind of pattern often considered is association rules, probabilistic rules stating that objects satisfying description A also satisfy description B with certain support and confidence. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule mining. The running results show that it is feasible and valuable to apply this method to mining the association rule and the improved algorithm can decrease related computation quantity in large scale and improve the efficiency of the algorithm. Finally, the simulation results of knowledge acquisition for fault diagnosis also show the validity of CFP algorithm.

scholarly journals Fault diagnosis of air-conditioning refrigeration system based on sparse autoencoder

2019 ◽  
Vol 14 (4) ◽  
pp. 487-492
Author(s):  
Zhiyi Wang ◽  
Jiachen Zhong ◽  
Jingfan Li ◽  
Cui Xia
Keyword(s):  
Fault Diagnosis ◽  
Air Conditioning ◽  
Large Scale ◽  
High Efficiency ◽  
Support Vector ◽  
Small Scale ◽  
Refrigeration System ◽  
Large Scale Data ◽  
Fault Features ◽  
Sparse Autoencoder

Abstract To overcome the drawbacks of using supervised learning to extract fault features for classification and low nonlinearity of the features in most of current fault diagnosis of air-conditioning refrigeration system, sparse autoencoder (SAE) is presented to extract fault features that are used as the input to the classifier and to achieve fault diagnosis for air-conditioning refrigeration system. The SAE structure is tuned by adjusting the number of hidden layers and nodes to build the optimal model, which is compared with the fault diagnosis model based on support vector machine. Results indicate that the indexes of the model combined with SAE, such as accuracy, precision and recall, are all improved, especially for the faults with high complexity. Besides, SAE shows high generalization ability with small-scale sample data and high efficiency with large-scale data. Obviously, the use of SAE can effectively optimize the diagnosis performance of the classifier.

Research and Implementation of an Expert System for Fault Diagnosis of Fan Based on Knowledge Rule

2011 ◽  
Vol 201-203 ◽  
pp. 1989-1992
Author(s):  
Lei Wang ◽  
Tian Zhong Sui ◽  
Yu Song ◽  
Hai Xiang Zhao ◽  
Bo Ran Zhuang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Relational Database ◽  
High Efficiency ◽  
Rule Based ◽  
Diagnosis System ◽  
Hybrid Reasoning ◽  
Fault Diagnosis System ◽  
And Function

An example of the rule-based expert system applied to the fan fault diagnosis is presented. The architecture and function of the fault diagnosis system are introduced. The expression of the fault diagnosis knowledge and the attribute of knowledge base based on the relational database have been studied. The hybrid reasoning technology was applied to the implementation of the diagnosis inference engine in the expert system. The presented fault diagnosis system is easy to modify the knowledge base with the experience accumulated in practice, and it has the advantages of expansibility, portability, concision, and high efficiency.

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