Multi Expert Knowledge Transfer for Metal Forging Fault Diagnosis

2009 ◽  
Author(s):  
N. Ravaille ◽  
J.L. Wybo
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Transfer ◽  
Expert Knowledge ◽  
Expert Knowledge Transfer

General Fault Monitoring and Diagnosis Expert System Based on Fault Tree and Multi-Sensor Information

2011 ◽  
Vol 121-126 ◽  
pp. 4481-4485
Author(s):  
Ai Yu Zhang ◽  
Xiao Guang Zhao ◽  
Lei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Expert Knowledge ◽  
Sensor Failure ◽  
Fault Trees ◽  
Intelligent Algorithms ◽  
Monitoring And Diagnosis ◽  
Backward Reasoning ◽  
Fault Monitoring ◽  
Sensor Information

Due to the limited generality of traditional fault diagnosis expert system and its low accuracy of extracting failure symptoms, a general fault monitoring and diagnosis expert system has been built. For different devices, users can build fault trees in an interactive way and then the fault trees will be saved as expert knowledge. A variety of sensors are fixed to monitor the real-time condition of the device and intelligent algorithms such as wavelet transform and neural network are used to assist the extraction of failure symptoms. On the basis of integration of multi-sensor failure symptoms, the fault diagnosis is realized through forward and backward reasoning. The simulation diagnosis experiments of NC device have shown the effectiveness of the proposed method.

Assessing the dynamics of knowledge transfer between masters and apprentices

2019 ◽  
Vol 36 (2) ◽  
pp. 30-32
Keyword(s):  
Knowledge Transfer ◽  
Design Methodology ◽  
Expert Knowledge ◽  
Evolutionary Game ◽  
Optimum Form ◽  
Content Type ◽  
Self Interest ◽  
Pertinent Information ◽  
Training Path ◽  
Practical Implications

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings This research paper concentrates on the deployment of asymmetric evolutionary game theory to reveal how innovative organizations best effect knowledge sharing by aligning the incentivized desire of masters to share their expert knowledge with the self-interest of apprentices who are highly motivated to accept that knowledge on an accelerated training path. These insights improve the strategic capacity of human resources teams to add value to their organization by encouraging the optimum form of knowledge transfer between masters and apprentices. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

A Research of Direction Expert System Technology for Aircraft Maintenance

2011 ◽  
Vol 403-408 ◽  
pp. 1692-1695
Author(s):  
Zhi Qiu ◽  
Shou Miao Yu ◽  
Zheng Wang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Web Service ◽  
Expert Knowledge ◽  
Sql Server ◽  
Aircraft Maintenance ◽  
System Technology ◽  
Sql Server Database ◽  
Database Technology

An direction expert system for aircraft maintain with expert knowledge graphical building, three layers distributed web service, distance fault diagnosis and gather experience ability is introduced here. It is developed based on Delphi 6.0 utilizing multilayer web service, CLIPS expert system and SQL SERVER database technology.

scholarly journals Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8168
Author(s):  
Lihao Ye ◽  
Xue Ma ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Transfer ◽  
Frequency Domain ◽  
Rotating Machinery ◽  
Domain Model ◽  
Small Samples ◽  
Target Domain ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Deep Cnn

Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, this paper proposes a fault diagnosis method based on knowledge transfer in deep learning. First, we describe the data collected during the operation as a two-dimensional image with both time and frequency-domain characteristics. Second, we transform the trained source domain model into a shallow model suitable for small samples in the target domain, and we train the shallow model with small samples with labels. Third, we input a large number of unlabeled samples into the shallow model, and the output result of the system is regarded as the label of the input sample. Fourth, we combine the original data and the data annotated by the shallow model to train the new deep CNN fault diagnosis model so as to realize the migration of knowledge from the expert system to the deep CNN. The newly built deep CNN model is used for the online fault diagnosis of rotating machinery. The FFCNN-SVM shallow model tagger method proposed in this paper compares the fault diagnosis results with other transfer learning methods at this stage, and its correct rate has been greatly improved. This method provides new ideas for future fault diagnosis under small samples.

Research on Running Condition Recognition of Mechanical Equipment and Fault Diagnosis Method Based on Fuzzy Evaluation

2010 ◽  
Vol 146-147 ◽  
pp. 530-535
Author(s):  
Bao Ming Chai ◽  
Wei Jin Gao ◽  
Xue Pan Gao
Keyword(s):  
Mathematical Model ◽  
Fault Diagnosis ◽  
Evaluation Method ◽  
Expert Knowledge ◽  
Mechanical Equipment ◽  
Fuzzy Evaluation ◽  
State Recognition ◽  
Uncertainty Measurement ◽  
Calculation Results ◽  
Diagnosis Method

Apply fuzzy evaluation method to research on running state recognition of mechanical equipment and fault diagnosis, define concept of classification weight of index, give out calculation method. On the basis of analysis of fault datasheet that records expert knowledge, build mathematical model of recognition and evaluation on running condition of mechanical equipment based on uncertainty measurement. Calculation examples show the effectiveness of this method and the reliability of calculation results.

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