scholarly journals Outline of a fault diagnosis system for a large-scale board machine

2012 ◽  
Author(s):  
Sirkka-Liisa Jamsa-Jounela ◽  
Vesa-Matti Tikkala ◽  
Alexey Zakharov ◽  
Octavio Pozo Garcia
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Diagnosis System ◽  
Fault Diagnosis System

Distributed Monitoring and Intelligent Fault Diagnosis System for Large-Scale Electro-Hydraulic Devices

2015 ◽  
Vol 779 ◽  
pp. 163-168
Author(s):  
Gui You Hao ◽  
Jie Cheng ◽  
Zong Gui Zheng ◽  
Zhi Gang Yu ◽  
Gang Wu
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Large Scale ◽  
Sensor Data ◽  
Distributed Monitoring ◽  
Diagnosis System ◽  
Fusion Methods ◽  
Level Information ◽  
Fault Diagnosis System ◽  
Hydraulic Devices

In this paper, a distributed monitoring and intelligent diagnosis system is designed for large-scale electro-hydraulic devices, based on the utilization of information fusion technology. As a result of using multi-sensor data level, feature level and decision-making level information fusion methods, both precision and real-time performance of condition monitoring and fault diagnosis have been greatly improved, which leads to an advance in the accuracy of monitoring and diagnosis.

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.

Coordination Fault Diagnosis Based on RBF

2013 ◽  
Vol 329 ◽  
pp. 278-282
Author(s):  
Rui Hua Xu ◽  
Zheng Zhou Wang ◽  
Ya Dong Yan ◽  
Cai Wen Ma
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
High Accuracy ◽  
Case Based Reasoning ◽  
Rule Based ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Simulation Results ◽  
Self Learning ◽  
Case Based

In large-scale complex system, The establishment of a fast, accurate fault diagnosis system is more difficult because there exist many uncertain elements between the fault cause and the fault sign .A fault diagnosis system is established based on RBF cloud neural network ,the RBR (rule-based reasoning) and the CBR (case-based reasoning).The fault diagnosis system not only has the advantages of self-learning, high accuracy, randomness, fuzziness, etc ,and has the advantages of independently of mathematical model ,rich knowledge representation, mighty problem solving ability, etc. Theoretical analysis and simulation results show that the system is feasible and effective for fast and accurate fault positioning of complex systems.

scholarly journals A Large Scale Fault Diagnosis System for a Hot Strip Mill

1998 ◽  
Vol 31 (23) ◽  
pp. 189-194
Author(s):  
Matti Kurki ◽  
Tapio Taipale ◽  
Panu Korpipää ◽  
Esko Ahola
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Hot Strip Mill ◽  
Diagnosis System ◽  
Hot Strip ◽  
Fault Diagnosis System

Study on Networking and Intelligent Fault Diagnosis System for Large-Scale Rotating Machinery

2012 ◽  
Vol 588-589 ◽  
pp. 178-184
Author(s):  
Jie Liu ◽  
Fang Xia Hu
Keyword(s):  
Fault Diagnosis ◽  
Remote Monitoring ◽  
Large Scale ◽  
Online Monitoring ◽  
Rotating Machinery ◽  
Functional Modules ◽  
Mixed Structure ◽  
Diagnosis System ◽  
Monitoring And Diagnosis ◽  
Fault Diagnosis System

A networking and intelligent online monitoring and fault diagnosis system for large-scale rotating machinery is developed according to requirements of an iron & steel enterprise. On the aspect of networking, a mixed structure of C/S and B/S is adopted, and the system integrates local online monitoring and diagnosis, remote monitoring and diagnosis, and remote diagnosis center. On the aspect of intelligent diagnosis, a multi-symptom comprehensive parallel diagnosis technology is adopted based on expert system, neural network and fuzzy logic. Finally, main functional modules and its realization are introduced. Application shows that the system runs normally, and the expected objective is achieved.

scholarly journals Outline of a fault diagnosis system for a large-scale board machine

2012 ◽  
Vol 65 (9-12) ◽  
pp. 1741-1755 ◽  
Author(s):  
Sirkka-Liisa Jämsä-Jounela ◽  
Vesa-Matti Tikkala ◽  
Alexey Zakharov ◽  
Octavio Pozo Garcia ◽  
Helena Laavi ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Diagnosis System ◽  
Fault Diagnosis System

scholarly journals Application of Computer Algorithm in Fault Diagnosis System of RM Equipment

2021 ◽  
Vol 2143 (1) ◽  
pp. 012033
Author(s):  
Xinfeng Zhang ◽  
Guanglu Yang ◽  
Yan Cui ◽  
Xinfeng Wei ◽  
Wensheng Qiao
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Automatic Monitoring ◽  
Mechanical Equipment ◽  
Computer Algorithms ◽  
Body Segment ◽  
Turbine Generator ◽  
Diagnosis System ◽  
Core Components ◽  
Fault Diagnosis System

Abstract At present, modern mechanical equipment is gradually developing towards large-scale and intelligent, which leads to more and more complex equipment structure. Therefore, people have higher and higher requirements for intelligent fault diagnosis of mechanical equipment, which leads to the application of various algorithms to mechanical equipment. Among them, rotating machinery (hereinafter referred to as RM) mainly relies on rotating action to complete specific functions, such as gearbox, gas turbine, generator and engine, which are often the core components of mechanical equipment. Therefore, the FSGS (hereinafter referred to as FSGS) of RM equipment has become a very key link in system design and maintenance, which requires designers to constantly overcome the original intelligent diagnosis system. Through a variety of deep learning algorithms, we can improve the diagnosis efficiency of automatic monitoring and diagnosis equipment, which can also reduce the loss caused by untimely diagnosis. Firstly, this paper analyzes the types of application of computer algorithms in the fault body segment system of RM equipment. Then, this paper analyzes an algorithm, which can better improve the diagnosis efficiency of the equipment.

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