scholarly journals Simulation Platform for Fault Diagnosis

2022 ◽  
pp. 45-58
Author(s):  
Jing Wang ◽  
Jinglin Zhou ◽  
Xiaolu Chen
Keyword(s):  
Fault Diagnosis ◽  
Process Simulation ◽  
Fermentation Process ◽  
Industrial Process ◽  
Simulation System ◽  
Fault Classification ◽  
Simulation Platform ◽  
Multivariate Statistical ◽  
Fault Monitoring ◽  
Statistical Analysis Methods

AbstractThe previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well as the monitoring processes when used for fault diagnosis. In order to validate the effectiveness of data-driven multivariate statistical analysis methods in the field of fault diagnosis, it is necessary to conduct the corresponding fault monitoring experiments. Therefore this chapter introduces two kinds of simulation platform, Tennessee Eastman (TE) process simulation system and fed-batch Penicillin Fermentation Process simulation system. They are widely used as test platforms for the process monitoring, fault classification, and identification of industrial process. The related experiments based on PCA, CCA, PLS, and FDA are completed on the TE simulation platforms.

Design of penicillin fermentation process simulation system

2011 ◽  
Author(s):  
Xiaoyu Qi ◽  
Zhonghu Yuan ◽  
Xiaoxuan Qi ◽  
Wenqi Zhang
Keyword(s):  
Process Simulation ◽  
Fermentation Process ◽  
Simulation System ◽  
Penicillin Fermentation

scholarly journals Simple Industrial Process Simulation with Mindstorms NXT as Tool in Automation Lessons

2014 ◽  
Vol 2 (10) ◽  
pp. 194-203
Author(s):  
Bruno Alves Maia ◽  
Marcelo H. Stoppa
Keyword(s):  
Process Simulation ◽  
Industrial Process ◽  
Simulation System ◽  
The Body ◽  
Automated System ◽  
Automated Manufacturing ◽  
Programmable Logic ◽  
Basic Product ◽  
Technological Tools ◽  
Two Phases

This work presents the development of a simulation prototype for an automated manufacturing process using the Mindstorms NXT LEGO© robotic kit. This process consists of assembling a basic product, namely a mini car done with LEGO© pieces, into two phases. First, the coupling of the body and chassis and after this, the separation of assembled products by colour. The intent here is to show that it is possible to create an automated system with NXT, like a mockup, that can be simulate a real system, and which has similarity to an automated system using a Programmable Logic Controller (PLC), with the advantage of being more practical and cheaper than an educational simulation system with PLC. The aims here is present an adaptable tool, to auxiliar the automation teaching and to motivate to use of new technological tools in classroom.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

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.

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