scholarly journals Research on fault diagnosis of thermodynamic system based on the network model of internet of things

2019 ◽  
Vol 23 (5 Part A) ◽  
pp. 2685-2693
Author(s):  
Fangcheng He ◽  
Pengcheng Wei
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Network Model ◽  
Mechanical Damage ◽  
Optimal Solution ◽  
Simulated Data ◽  
Thermodynamic System ◽  
Fault Parameters ◽  
Gas Path Fault ◽  
Diagnosis Criterion

Aiming at the problem that the traditional diagnosis model is difficult to be established accurately, a fault diagnosis algorithm based on the fault diagnosis criterion is constructed to study the fault diagnosis of thermodynamic system based on the network model of internet of things. By analyzing the fault parameters of the equipment system, the algorithm establishes the fault matrix, calculates the mapping relation function corresponding to the states with unknown and known matrix, and obtains the optimal solution of the objective function. It solves the problem that the traditional diagnosis scheme is difficult to accurately diagnose the unknown model. By analyzing the cause and mechanism of the system fault, the diagnosis criterion of each kind of fault is determined. The fault matrix is established by calculation and judgment. The simulation experiment of gas path fault shows that the criterion of turbine blade mechanical damage fault is that the turbine efficiency is reduced by 5%, which is consistent with the theoretical analysis. This shows that the proposed algorithm is effective and the simulated data can be used as technical support for fault diagnosis of similar thermodynamic systems.

A Seven-Layered Model Architecture, Network Model, Protocol Stack, Security, Application, Issues and Challenges in Internet of Vehicle

2020 ◽  
Vol 14 ◽  
Author(s):  
Intyaz Alam ◽  
Sushil Kumar ◽  
Pankaj Kumar Kashyap
Keyword(s):  
Internet Of Things ◽  
Network Model ◽  
Transportation Systems ◽  
Vehicle Speed ◽  
Protocol Stack ◽  
Seamless Integration ◽  
Communication Architecture ◽  
Smart Transportation ◽  
Smart Healthcare ◽  
Essential Components

Background: Recently, Internet of Things (IoT) has brought various changes in the existing research field by including new areas such as smart transportation, smart home facilities, smart healthcare, etc. In smart transportation systems, vehicles contain different components to access information related to passengers, drivers, vehicle speed, and many more. This information can be accessed by connecting vehicles with Internet of Things leading to new fields of research known as Internet of Vehicles. The setup of Internet of Vehicle (IoV) consists of many sensors to establish a connection with several other sensors belonging to different environments by exploiting different technologies. The communication of the sensors faces a lot of challenging issues. Some of the critical challenges are to maintain security in information exchanges among the vehicles, inequality in sensors, quality of internet connection, and storage capacity. Objective: To overcome the challenging issues, we have designed a new framework consisting of seven-layered architecture, including the security layered, which provides seamless integration by communicating the devices present in the IoV environment. Further, a network model consisting of four components such as Cloud, Fog, Connection, and Clients has been designed. Finally, the protocol stack which describes the protocol used in each layer of the proposed seven-layered IoV architecture has been shown. Methods: In this proposed architecture, the representation and the functionalities of each layer and types of security have been defined. Case studies of this seven-layer IoV architecture have also been performed to illustrate the operation of each layer in real-time. The details of the network model including all the elements inside each component, have also been shown. Results: We have discussed some of the existing communication architecture and listed a few challenges and issues occurring in present scenarios. Considering these issues, which is presently occurring in the existing communication architecture. We have developed the seven-layered IoV architecture and the network model with four essential components known as the cloud, fog, connection, and clients. Conclusion: This proposed architecture provides a secure IoV environment and provides life safety. Hence, safety and security will help to reduce the cybercrimes occurring in the network and provides good coordination and communication of the vehicles in the network.

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.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

scholarly journals Power transformer fault diagnosis system based on Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Guoshi Wang ◽  
Ying Liu ◽  
Xiaowen Chen ◽  
Qing Yan ◽  
Haibin Sui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
Power Transformer ◽  
Fault System ◽  
Ratio Method ◽  
Diagnosis System ◽  
Network Training ◽  
Fault Diagnosis System ◽  
Training Error ◽  
Transformer Fault

Abstract Transformer is the most important equipment in the power system. The research and development of fault diagnosis technology for Internet of Things equipment can effectively detect the operation status of equipment and eliminate hidden faults in time, which is conducive to reducing the incidence of accidents and improving people's life safety index. Objective To explore the utility of Internet of Things in power transformer fault diagnosis system. Methods A total of 30 groups of transformer fault samples were selected, and 10 groups were randomly selected for network training, and the rest samples were used for testing. The matter-element extension mathematical model of power transformer fault diagnosis was established, and the correlation function was improved according to the characteristics of three ratio method. Each group of power transformer was diagnosed for four months continuously, and the monitoring data and diagnosis were recorded and analyzed result. GPRS communication network is used to complete the communication between data acquisition terminal and monitoring terminal. According to the parameters of the database, the working state of the equipment is set, and various sensors are controlled by the instrument driver module to complete the diagnosis of transformer fault system. Results The detection success rate of the power transformer fault diagnosis system model established in this paper is as high as 95.6%, the training error is less than 0.0001, and it can correctly identify the fault types of the non training samples. It can be seen that the technical support of the Internet of Things is helpful to the upgrading and maintenance of the power transformer fault diagnosis system.

scholarly journals Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1920 ◽  
Author(s):  
Juanli Li ◽  
Jiacheng Xie ◽  
Zhaojian Yang ◽  
Junjie Li
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
The Internet ◽  
Internet Of Things Environment ◽  
Diagnosis Method ◽  
Mine Hoist ◽  
The Internet Of Things

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.

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