Research on BIT Intelligence Maintenance Decision of Weapon Materiel

2011 ◽  
Vol 128-129 ◽  
pp. 224-228
Author(s):  
Jian Xin Huang ◽  
Ya Qin Bian
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Fault Diagnosis ◽  
Expert System ◽  
System Organization ◽  
System Structure ◽  
Concurrent Design ◽  
Intelligence Theory ◽  
Maintenance Decision ◽  
Organization Framework

Considering the weapon materiel maintenance condition, intelligence theory and method such as expert system and neural network is introduced in BIT technology. BIT intelligence maintenance decision in materiel maintenance field is discussed. The realization of materiel intelligence BIT reflects the concurrent design concept of system exploitation. Level integrated intelligence BIT system organization framework is adopted. BIT comprehensive decision-making and maintenance expert system structure is designed, and its implementation plan based on BIT is offered, which can enhance the fault diagnosis capability of modern complicated weapon materiel.

Application Overview of Neural Network in Fault Diagnosis

2014 ◽  
Vol 697 ◽  
pp. 419-424
Author(s):  
Ze Fan Cai ◽  
Dao Ping Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Development Trend ◽  
System Structure ◽  
Bp Network ◽  
Rbf Network ◽  
The Third ◽  
The Neural Network ◽  
The Development Trend

This paper introduces the system structure of neural network in fault diagnosis, and summarizes some applications of neural network in fault diagnosis. The most commonly used neural network in fault diagnosis is BP network. The second is RBF network and the third is ART. For each neural network, the paper will discuss the neural network, and the introduce some applications. It also introduces the combination of neural networks and other techniques. In the last part, this paper points out the development trend of the neural network in fault diagnosis.

Orbit Shape Automatic Recognition Based on Artificial Neural Network

2005 ◽  
Author(s):  
Dongmei Du ◽  
Qing He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Recognition Performance ◽  
Shape Recognition ◽  
Vibration Monitoring ◽  
Moment Invariants ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Orbit is a significant symptom in the fault diagnosis of rotating machine. The orbit is a 2-D image and can be described by moment invariants, the shape property of 2-D image, which is a description with translating-, rotating-, and scaling-invariants for 2-D image. The descriptive method of orbit image is investigated and an automatic orbit shape recognition based on artificial neural network (ANN) with moment invariants is proposed in this paper. The ANN of orbit shape recognition is trained by the training patterns generated by computer simulation for plenty of orbit shapes. It is shown that the trained ANN is of good recognition performance and generalization capability when applied to recognition of the measured orbits. This method can be used to the intelligent expert system of fault diagnosis to obtain automatically online orbit symptom in shafts vibration monitoring of turbine generator, which will improve the automatization of obtaining fault symptom and the automatic diagnosis in the expert system.

An Expert System Shell for Fault Diagnosis

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 669-674 ◽  
Author(s):  
Jie Yang ◽  
Chenzhou Ye ◽  
Xiaoli Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Representation ◽  
Expert Systems ◽  
Knowledge Representation And Reasoning ◽  
Multiple Models ◽  
Object Model ◽  
Knowledge Models ◽  
Expert System Shell

Traditional expert systems for fault diagnosis have a bottleneck in knowledge acquisition, and have limitations in knowledge representation and reasoning. A new expert system shell for fault diagnosis is presented in this paper to develop multiple knowledge models (object model, rules, neural network, case-base and diagnose models) hierarchically based on multiple knowledge. The structure of the expert system shell and the knowledge representation of multiple models are described. Diagnostic algorithms are presented for automatic modeling and hierarchical reasoning. It will be shown that the expert system shell is very effective in building diagnostic expert systems.

Vibration Fault Diagnosis for Hydroelectric Generating Units Based on the Modified Cuckoo Search Algorithm and Evidence Theory

2019 ◽  
Vol 13 (3) ◽  
pp. 281-288
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Yan Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Evidence Theory ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
System Structure ◽  
Hybrid Scheme ◽  
Independent Evidence

Background: In view of the complex system structure and uncertain factors in the fault diagnosis of hydroelectric generating units (HGU), it is a difficult problem to design the diagnosis method rationally. Objective: An attempt is made to employ multi-source feature information to improve the accuracy of fault diagnosis, and the effectiveness of the proposed scheme is verified by using a diagnostic example. Methods: Through the research on recent papers and patents related to fault diagnosis of the HGU, a hybrid scheme based on the modified cuckoo search algorithm, back-propagation (BP) neural network and evidence theory are proposed. For this modified version named cuckoo search with fitness information (CSF), the step factor is adaptively tuned using the fitness value. Next, three diagnostic models based on BP neural network trained by CSF are used for primary diagnosis. These diagnostic results are then used as the independent evidence, and the fusion decision is made by using evidence theory. Results: Experimental results show that CSF algorithm is better than the original cuckoo search (CS) and its three variants, and the hybrid method has the highest diagnostic accuracy. Conclusion: The proposed hybrid scheme has strong robustness and fault tolerance, and can effectively classify the vibration faults of hydroelectric generating units

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