Vibration sensor based intelligent fault diagnosis system for large machine unit in petrochemical industry

Zhang, Qinghua; Qin, Aisong; Shu, Lei; Shao, Longqiu

doi:10.1109/iwcmc.2013.6583757

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It brings people convenience while also the network virus [1][2] affecting network security. According to the real-time performance of submarine optical fiber network fault diagnosis [3][4][5], a big data attribute selection method based on rough set of submarine optical fiber network fault diagnosis database is proposed [6]..…”

Section: Introductionmentioning

confidence: 99%

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Chen

2017

Polish Maritime Research

View full text Add to dashboard Cite

At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM) for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight

show abstract

Section: Introductionmentioning

confidence: 99%

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Chen

2017

Polish Maritime Research

View full text Add to dashboard Cite

show abstract

Visualization of Mechanical Microvibration and Machine Diagnosis

et al. 2021

View full text Add to dashboard Cite

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

Xing-hua

Wan

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

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.

show abstract

Vibration sensor based intelligent fault diagnosis system for large machine unit in petrochemical industry

Cited by 3 publications

References 2 publications

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Visualization of Mechanical Microvibration and Machine Diagnosis

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

Contact Info

Product

Resources

About