Application of Computer Algorithm in Fault Diagnosis System of RM Equipment

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.

Download Full-text

Probabilistic Model-Based Fault Diagnosis of the Rotor System

ASME 2007 Power Conference ◽

10.1115/power2007-22072 ◽

2007 ◽

Author(s):

Jiye Shao ◽

Rixin Wang ◽

Jingbo Gao ◽

Minqiang Xu

Keyword(s):

Fault Diagnosis ◽

Bayesian Network ◽

Network Model ◽

Rotating Machinery ◽

Rotor System ◽

Bayesian Network Model ◽

Diagnosis System ◽

Model Based ◽

Core Components ◽

Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Download Full-text

Vibration Monitoring and Fault Diagnosis System of Turbine-Generator

ASME 2007 Power Conference ◽

10.1115/power2007-22020 ◽

2007 ◽

Author(s):

Dongmei Du ◽

Qing He ◽

Hong Li

Keyword(s):

Fault Diagnosis ◽

Power Plants ◽

Vibration Monitoring ◽

Turbine Generator ◽

Diagnosis System ◽

Java Applet ◽

Java Technology ◽

Fault Diagnosis System ◽

The Difference ◽

Batch Data

It is very important to monitor vibration and diagnose fault for the operating safety of turbine-generator. The remote monitor and diagnosis via the cyber-based technology is a necessity. The difference between browser/server mode and client/server mode is discussed. There are many advantages of applying Java technology. Using Java, a vibration monitoring and fault diagnosis system of turbine-generator based on browser/server mode is developed. The functions as well as the structure of the whole system are analyzed. Online transmission of batch data via Internet is presented, especially for different program languages. Java Applet technology is used to develop client program. With double-buffer method, a lot of graphic interfaces of dynamic making online are presented, which are not blinking. It is proved that the system is already adopted and functions well in several power plants.

Download Full-text

Fault diagnosis system for hydraulic turbine generator

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes1990.122.4_492 ◽

2002 ◽

Vol 122 (4) ◽

pp. 492-497 ◽

Cited By ~ 2

Author(s):

Yukiharu Ohga ◽

Kazuo Moriguchi ◽

Seiji Honda ◽

Hiroto Nakagawa

Keyword(s):

Fault Diagnosis ◽

Hydraulic Turbine ◽

Turbine Generator ◽

Diagnosis System ◽

Fault Diagnosis System

Download Full-text

Fault Diagnosis System Research of Large-scale Linear Vibrating Screen

DEStech Transactions on Engineering and Technology Research ◽

10.12783/dtetr/icmca2017/12364 ◽

2017 ◽

Author(s):

Bin LIU ◽

Qing-hui ZHU

Keyword(s):

Fault Diagnosis ◽

Large Scale ◽

System Research ◽

Diagnosis System ◽

Vibrating Screen ◽

Fault Diagnosis System

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.779.163 ◽

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.

Download Full-text

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.201-203.956 ◽

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.

Download Full-text

Coordination Fault Diagnosis Based on RBF

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.329.278 ◽

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.

Download Full-text