基于Internet的壓縮機遠程監(jiān)測與故障診斷技術(shù)研究
發(fā)布時間：2019-06-26 12:51
【摘要】：壓縮機是工業(yè)領(lǐng)域的關(guān)鍵機械設(shè)備,一旦其出現(xiàn)異�；蛲话l(fā)狀況,往往會造成整套設(shè)備或機組的癱瘓,為企業(yè)和社會造成巨大的經(jīng)濟損失,甚至會引起重大的人員傷亡事故。所以及時的狀態(tài)監(jiān)測及故障診斷對安全生產(chǎn)有著重要意義。同時隨著壓縮機機組的復雜化、網(wǎng)絡化的發(fā)展,傳統(tǒng)的本地監(jiān)測與診斷模式已很難滿足診斷的需求,所以遠程化與智能化成為了當前研究的熱點。本文以螺桿壓縮機為研究對象,建立壓縮機遠程監(jiān)測平臺以及智能故障診專家系統(tǒng)。 首先對遠程監(jiān)測平臺的總體架構(gòu)進行了研究,根據(jù)壓縮機監(jiān)測的實際需求,確定了其網(wǎng)絡結(jié)構(gòu),并通過對當前軟件平臺結(jié)構(gòu)的對比,確定了基于RIA的互聯(lián)網(wǎng)應用框架,由CBX解決方案進行開發(fā),并選用SQL Server2000數(shù)據(jù)庫管理系統(tǒng)進行數(shù)據(jù)庫開發(fā)。另外對于系統(tǒng)平臺的網(wǎng)絡安全策略,分別在硬件與軟件上進行了相關(guān)研究。 在對螺桿機基本結(jié)構(gòu)及工作原理分析的基礎(chǔ)上,總結(jié)出螺桿壓縮機常見的故障。主要研究了基于振動信號的分析方法,包括時域分析、頻域分析以及時頻分析,并通過實測信號進行分析,以驗證分析方法的可行性。 傳統(tǒng)基于規(guī)則的專家系統(tǒng)存在著知識獲取“瓶頸”等問題,而神經(jīng)網(wǎng)絡因其具有并行、自適應性、自學習性等優(yōu)點,有效地解決了傳統(tǒng)專家系統(tǒng)遇到的問題。本文綜合其各自優(yōu)點,提出了神經(jīng)網(wǎng)絡專家系統(tǒng),分析并確定了神經(jīng)網(wǎng)絡系統(tǒng)的結(jié)構(gòu)。針對壓縮機故障種類繁多的特點,采用集成神經(jīng)網(wǎng)絡的思想,將各類故障由診斷子網(wǎng)絡進行診斷,最后再由決策網(wǎng)絡進行融合。在診斷子網(wǎng)絡的處理過程中,多傳感器的局部信息融合則由D-S信息融合完成。最后,對神經(jīng)網(wǎng)絡專家系統(tǒng)的知識庫進行了設(shè)計。 最后設(shè)計開發(fā)出基于internet的壓縮機遠程監(jiān)測及故障診斷系統(tǒng),其功能包括數(shù)據(jù)采集與通信、實時監(jiān)測、數(shù)據(jù)分析、信息查詢與管理、權(quán)限管理、報表管理以及專家系統(tǒng)等等。
[Abstract]:Compressor is the key mechanical equipment in the industrial field. Once it appears abnormal or unexpected situation, it will often cause the paralysis of the whole set of equipment or units, cause huge economic losses for enterprises and society, and even cause heavy casualties. Therefore, timely condition monitoring and fault diagnosis is of great significance to safety in production. At the same time, with the complexity of compressor units and the development of network, the traditional local monitoring and diagnosis model has been difficult to meet the needs of diagnosis, so remote and intelligent has become the focus of current research. In this paper, the remote monitoring platform and intelligent fault diagnosis expert system of screw compressor are established. Firstly, the overall architecture of remote monitoring platform is studied, and its network structure is determined according to the actual requirements of compressor monitoring. Through the comparison of the current software platform structure, the Internet application framework based on RIA is determined, which is developed by CBX solution, and the SQL Server2000 database management system is selected for database development. In addition, the network security strategy of the system platform is studied in hardware and software. Based on the analysis of the basic structure and working principle of screw compressor, the common faults of screw compressor are summarized. The analysis methods based on vibration signal, including time domain analysis, frequency domain analysis and time frequency analysis, are mainly studied, and the feasibility of the analysis method is verified by the measured signal analysis. There are some problems in the traditional rule-based expert system, such as the bottleneck of knowledge acquisition, and the neural network has the advantages of parallelism, adaptability and self-learning, which effectively solves the problems encountered by the traditional expert system. In this paper, based on their respective advantages, a neural network expert system is proposed, and the structure of the neural network system is analyzed and determined. According to the characteristics of many kinds of compressor faults, the idea of integrated neural network is adopted to diagnose all kinds of faults by diagnosis subnetwork, and finally to merge them by decision network. In the process of diagnosis subnetwork processing, multi-sensor local information fusion is completed by D / S information fusion. Finally, the knowledge base of neural network expert system is designed. Finally, a compressor remote monitoring and fault diagnosis system based on internet is designed and developed, which includes data acquisition and communication, real-time monitoring, data analysis, information query and management, authority management, report management and expert system.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP274;TH45
本文編號：2506192
[Abstract]:Compressor is the key mechanical equipment in the industrial field. Once it appears abnormal or unexpected situation, it will often cause the paralysis of the whole set of equipment or units, cause huge economic losses for enterprises and society, and even cause heavy casualties. Therefore, timely condition monitoring and fault diagnosis is of great significance to safety in production. At the same time, with the complexity of compressor units and the development of network, the traditional local monitoring and diagnosis model has been difficult to meet the needs of diagnosis, so remote and intelligent has become the focus of current research. In this paper, the remote monitoring platform and intelligent fault diagnosis expert system of screw compressor are established. Firstly, the overall architecture of remote monitoring platform is studied, and its network structure is determined according to the actual requirements of compressor monitoring. Through the comparison of the current software platform structure, the Internet application framework based on RIA is determined, which is developed by CBX solution, and the SQL Server2000 database management system is selected for database development. In addition, the network security strategy of the system platform is studied in hardware and software. Based on the analysis of the basic structure and working principle of screw compressor, the common faults of screw compressor are summarized. The analysis methods based on vibration signal, including time domain analysis, frequency domain analysis and time frequency analysis, are mainly studied, and the feasibility of the analysis method is verified by the measured signal analysis. There are some problems in the traditional rule-based expert system, such as the bottleneck of knowledge acquisition, and the neural network has the advantages of parallelism, adaptability and self-learning, which effectively solves the problems encountered by the traditional expert system. In this paper, based on their respective advantages, a neural network expert system is proposed, and the structure of the neural network system is analyzed and determined. According to the characteristics of many kinds of compressor faults, the idea of integrated neural network is adopted to diagnose all kinds of faults by diagnosis subnetwork, and finally to merge them by decision network. In the process of diagnosis subnetwork processing, multi-sensor local information fusion is completed by D / S information fusion. Finally, the knowledge base of neural network expert system is designed. Finally, a compressor remote monitoring and fault diagnosis system based on internet is designed and developed, which includes data acquisition and communication, real-time monitoring, data analysis, information query and management, authority management, report management and expert system.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP274;TH45
【引證文獻】
相關(guān)碩士學位論文 前1條
1 王芹芹;基于Internet的簡易智能小車監(jiān)控系統(tǒng)設(shè)計[D];華中師范大學;2013年
，本文編號：2506192
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