scholarly journals Remote Vibration Monitoring and Fault Diagnosis System of Synchronous Motor Based on Internet of Things Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xinghua Yuan ◽  
Yuling He ◽  
Shuting Wan ◽  
Minghao Qiu ◽  
Hongchun Jiang
Keyword(s):  
Fault Diagnosis ◽  
Internet Of Things ◽  
High Efficiency ◽  
Vibration Monitoring ◽  
Signal Acquisition ◽  
Rotating Machines ◽  
Diagnosis System ◽  
Synchronous Motors ◽  
Fault Diagnosis System ◽  
Internet Of Things Technology

With the rapid growth of the country’s economy and the rapid expansion of the industrial scale, the engine is developing towards high speed, high efficiency, and high power density. Synchronous motors are an important part of the power provided by large-scale chemical enterprises. The protection and control of synchronous motors are related to the long-term and safe operation of enterprise electrical equipment. The purpose of this paper is to realize the remote vibration monitoring and fault diagnosis of multiple rotating machines and real-time online monitoring and data storage function of the vibration state of the monitored equipment. The operation is simple and stable, and therefore, the problem of the equipment can be found at the first time, which provides forward operation of the equipment for a long time. In this paper, vibration caused by unbalanced mechanical equipment is not normally monitored remotely. Taking rigid rotor rotating machine as the research object, we adopt “Web server-database server-client” structure, the structure is the core software and hardware system design, and the application of Internet of Things technology enables users to remotely monitor and analyze the vibration state of multiple rotating mechanical devices at the same time. Hardware design mainly includes processor, function chip, sensor selection, filter circuit, adaptive sampling frequency signal acquisition circuit, and temperature measurement circuit design. Software design mainly includes main program design, signal acquisition subroutine, calibration subroutine, unbalanced calculation subroutine, and GPRS network communication subroutine. Finally, the function and stability of the whole system are verified through multiple experimental analyses. The objective has finally been achieved. The remote vibration monitoring and fault diagnosis system of the rotating machines designed on this paper is of low cost and high efficiency, simple operation, and high stability, and it is essential to identify and eliminate equipment errors in time.

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.

Based on the Online Monitoring and Fault Diagnosis System of the Main Hoist for Mine

2010 ◽  
Vol 42 ◽  
pp. 250-254
Author(s):  
Zhi Yong Pan ◽  
Quan Cai Wang ◽  
Wei Hong Ren
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Online Monitoring ◽  
Signal Acquisition ◽  
Diagnosis System ◽  
Current Voltage ◽  
Fault Diagnosis System ◽  
Real Time Database ◽  
Signal Acquisition And Processing ◽  
Main Hoist

According to the reality, an online monitoring and fault diagnosis system of the main hoist for Mine was designed in this article. The system adopts the signal acquisition and processing, fault diagnosis, Web visualization, network real-time database and other related technologies, Real-time monitoring the current, voltage, temperature, speed, vibration and other parameters of the main elevators to Achieve the goals that Increasing efficiency by downsizing, protecting the safe operation of equipment, reducing the maintenance costs.

Vibration Monitoring and Fault Diagnosis System of Turbine-Generator

2007 ◽  
Author(s):  
Dongmei Du ◽  
Qing He ◽  
Hong Li
Keyword(s):  
Fault Diagnosis ◽  
Power Plants ◽  
Vibration Monitoring ◽  
Turbine Generator ◽  
Diagnosis System ◽  
Java Applet ◽  
Java Technology ◽  
Fault Diagnosis System ◽  
The Difference ◽  
Batch Data

It is very important to monitor vibration and diagnose fault for the operating safety of turbine-generator. The remote monitor and diagnosis via the cyber-based technology is a necessity. The difference between browser/server mode and client/server mode is discussed. There are many advantages of applying Java technology. Using Java, a vibration monitoring and fault diagnosis system of turbine-generator based on browser/server mode is developed. The functions as well as the structure of the whole system are analyzed. Online transmission of batch data via Internet is presented, especially for different program languages. Java Applet technology is used to develop client program. With double-buffer method, a lot of graphic interfaces of dynamic making online are presented, which are not blinking. It is proved that the system is already adopted and functions well in several power plants.

Research and Implementation of an Expert System for Fault Diagnosis of Fan Based on Knowledge Rule

2011 ◽  
Vol 201-203 ◽  
pp. 1989-1992
Author(s):  
Lei Wang ◽  
Tian Zhong Sui ◽  
Yu Song ◽  
Hai Xiang Zhao ◽  
Bo Ran Zhuang
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Relational Database ◽  
High Efficiency ◽  
Rule Based ◽  
Diagnosis System ◽  
Hybrid Reasoning ◽  
Fault Diagnosis System ◽  
And Function

An example of the rule-based expert system applied to the fan fault diagnosis is presented. The architecture and function of the fault diagnosis system are introduced. The expression of the fault diagnosis knowledge and the attribute of knowledge base based on the relational database have been studied. The hybrid reasoning technology was applied to the implementation of the diagnosis inference engine in the expert system. The presented fault diagnosis system is easy to modify the knowledge base with the experience accumulated in practice, and it has the advantages of expansibility, portability, concision, and high efficiency.

scholarly journals Power Transformer Fault Diagnosis System Based on Internet of Things

2020 ◽  
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 0.000 l, 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 Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

2021 ◽  
Vol 10 (1) ◽  
pp. 245-254
Author(s):  
Xiaoran Zhang ◽  
Kantilal Pitambar Rane ◽  
Ismail Kakaravada ◽  
Mohammad Shabaz
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Diagnosis ◽  
Internet Of Things ◽  
Machine Learning Algorithms ◽  
Vibration Monitoring ◽  
Wireless Sensor ◽  
Industrial Wireless Sensor Networks ◽  
Industrial Automation ◽  
Internet Of Things Technology

Abstract Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.

Research of Large Excavator Multidimensional Intelligent Vibration Monitoring and Fault Diagnosis System

2013 ◽  
Vol 706-708 ◽  
pp. 928-931
Author(s):  
Ying Li ◽  
Zheng Hua Ru ◽  
Yi Ru
Keyword(s):  
Fault Diagnosis ◽  
Online Monitoring ◽  
Vibration Monitoring ◽  
Life Safety ◽  
Remote Consultation ◽  
Security Risks ◽  
Diagnosis System ◽  
Vibration Detection ◽  
Fault Diagnosis System

Excavator vibration speed not only directly affects the life length of excavator, but also contains security risks which even relate to the life safety. This article focuses on the Large Excavator Multidimensional Intelligent Vibration Monitoring and Fault Diagnosis System, which allows the excavator to perform vibration detection and warning of the key components under normal using state, and realizes online monitoring as well as remote consultation.

Development of Portable Fault Diagnosis System Based on SR

2016 ◽  
Vol 693 ◽  
pp. 1567-1569
Author(s):  
Pan Zhang ◽  
Peng Wang ◽  
Lu Yang Jin ◽  
Yu Long Wang
Keyword(s):  
Data Analysis ◽  
Fault Diagnosis ◽  
Embedded System ◽  
Data Storage ◽  
Signal Acquisition ◽  
Vibration Signals ◽  
Diagnosis System ◽  
Characteristic Quantity ◽  
Fault Diagnosis System ◽  
Dual Core

It is the hotpot to study the application of stochastic resonance (SR) in the field of mechanical fault diagnosis. The fault diagnosis system based on SR was developed, using “ARM+DSP” dual-core structure as hardware and embedded system as software, implemented signal acquisition, data storage, data analysis, waveform display and so on. The SR was adopted as characteristic quantity to analyze vibration signals for fault diagnosis. The experiment results showed that the function of portable fault diagnosis instrument is stable. It has veracity and validity.

scholarly journals Power Transformer Fault Diagnosis System Based on Internet of Things

2020 ◽  
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 0.000l, 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.

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