scholarly journals Application of Rotating Machinery Fault Diagnosis System Based on Improved WNN

2010 ◽  
Author(s):  
Huawei Zhang ◽  
Hao Pan
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Machinery Fault Diagnosis

Research on Rotating Machinery Fault Diagnosis System

2011 ◽  
Vol 55-57 ◽  
pp. 1310-1314
Author(s):  
Zheng Yao ◽  
Zhao Hua Wang
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Hybrid Method ◽  
Rotating Machinery ◽  
The Other ◽  
Bp Neural Networks ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Machinery Fault Diagnosis ◽  
Modern Industry

Fault diagnosis has been the research hotspot in the industry fields, but, with the gradual complication in modern industry equipments and systems, it is more hard to quickly diagnose complicated or exceptional faults. For overcoming the diagnosis weakness of traditional fault diagnosis methods in the rotating machinery, this paper presents a hybrid method that combines the wavelet with neural networks theory. Both the blindness of framework designs for BP neural networks and the problem of nonlinear optimizations were solved and this method was used in rotating machinery fault diagnosis. The research shows that this method is feasible and effective and can be applied to the other rotating machinery fault diagnosis.

Probabilistic Model-Based Fault Diagnosis of the Rotor System

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.

scholarly journals Fault Diagnosis System of Rotating Machinery Vibration Signal

2011 ◽  
Vol 15 ◽  
pp. 671-675 ◽  
Author(s):  
Lei You ◽  
Jun Hu ◽  
Fang Fang ◽  
Lintao Duan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Diagnosis System ◽  
Fault Diagnosis System

Research on B/S Mode-Based Rotating Machinery Remote Status Monitoring and Fault Diagnosis System

2011 ◽  
Vol 105-107 ◽  
pp. 747-750
Author(s):  
Li Guo Wang
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic System ◽  
Rotating Machinery ◽  
Laboratory Simulation ◽  
Diagnosis System ◽  
Status Monitoring ◽  
System Realization ◽  
Composition And Structure ◽  
Fault Diagnosis System ◽  
Realization Process

This paper discusses the composition and structure of B/S mode-based rotating machinery remote status monitoring and fault diagnosis system, builds the system network model, makes detailed introduction of fault diagnosis mode, system hardware composition, software platform and system realization process, and carries out laboratory simulation on the feasibility of remote monitoring diagnostic 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 Software design of rotating machinery fault diagnosis system based on deep learning

2021 ◽  
Vol 260 ◽  
pp. 03006
Author(s):  
Xiaofeng He ◽  
Xiaofeng Liu ◽  
Xiulian Lu ◽  
Lipeng He ◽  
Yunxiang Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Diagnosis System ◽  
Training Requirements ◽  
Learning Framework ◽  
Software Interface ◽  
Python Language ◽  
Fault Diagnosis System

With the development of Industry 4.0, in order to meet the needs of intelligent fault diagnosis of rotating machinery in the industrial field, this paper developed a fault diagnosis system for rotating machinery based on deep learning and wavelet transform methods. The system is based on the Python language and mainly combines the PyQt graphical interface framework and the TensorFlow machine learning framework to complete the training requirements for historical or online fault data, and perform online monitoring and diagnosis of equipment operating conditions. The diagnostic accuracy of the system test results is more than 95%, the software interface is friendly, the algorithm generalization ability is good, and the reliability is strong. It provides guidance for the diagnosis of rotating machinery.

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