Key Engineering Materials Vols. 293-294 (2005) pp 365-372 online at http://www.scientific.net (2005) Trans Tech Publications, Switzerland Online available since 2005/Sep/15 Internet/Intranet Based Remote Condition Monitoring and Fault Diagnosis Scheme and System for Steam Turboset Yongyong He 1 Wenxiu Lu 2 Fulei Chu 3 Department of Precision Instruments, Tsinghua University, Beijing 100084, P.R.China 1 2 heyy@mail.tsinghua.edu.cn luwenxiu@mail.tsinghua.edu.cn 3 Chul@mail.tsinghua.edu.cn Keywords: internet/intranet, condition monitoring, fault diagnosis, web/server, remote diagnosis Abstract: The steam turboset is the key equipment of the electric power system. Thus, it is very important and necessary to monitor and diagnose the running condition and the faults of the steam turboset for the safe and normal running of the electric power system. In this paper, the Internet/Intranet based remote condition monitoring and fault diagnosis scheme is proposed. The corresponding technique and methods are discussed in detail. And a real application system is developed for the 300MW steam turboset. In this scheme, the system is built on the Internet/Intranet and the Client/Server construction and Web/Server model are adopted. The proposed scheme can guarantee real-time data acquisition and on-line condition analysis simultaneously. And especially, the remote condition monitoring and fault diagnosis can be implemented effectively. The developed system has been installed in a power plant of China. And the plant has obtained great economic benefits from it. Introduction The electric power industry is the fundamental industry for the national economy. Thus, the running condition of the electric power system has a great influence on the performance and development of the national economy. The steam turboset is the key equipment of the electric power system. Apparently, it is very important and necessary to monitor and diagnose the running condition and faults of the steam turboset for the safe and normal running of the electric power system especially because of the steam turboset evolving in the direction of high-parameter, high-efficiency, high-power and high-automation [1-2]. In addition, to improve the maintenance efficiency and service efficiency of the steam turboset, the running condition based maintenance strategy has been proposed to complement or even replace the traditional scheduled maintenance strategy. And obviously, the condition monitoring and fault diagnosis of the steam turboset is the premise for such reformation of the maintenance strategy. Just because of above two points, the importance and the necessity of the condition monitoring and fault diagnosis of the steam turboset has obtained more and more recognition from the plants and the research society. And the power plants have put forward more and more urgent demand for the condition monitoring and fault diagnosis technique for the steam turboset [3]. Fortunately, with the development of the relevant techniques, such as electric and electronic technique, computer software and hardware technique, signal processing technique and artificial intelligence technique, the mechanical fault diagnosis technique has obtained great progress during last half century. For the rotating machinery, such as the steam turboset, many effective diagnosis methods and strategies have been developed. And the mechanism of the frequent faults has become clearer and clearer. All these achievements guarantee the All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of the publisher: Trans Tech Publications Ltd, Switzerland, www.ttp.net. (ID: 130.203.133.34-16/04/08,09:45:55)
366 Damage Assessment of Structures VI execution of the condition monitoring and fault diagnosis for the steam turboset. Actually, many systems have been developed for the steam turboset and obtained successful application in the power plants [1, 3, 4, 5, 6]. However, with the proposition and execution of the condition based maintenance strategy, the enterprises put forward new demands for the condition monitoring and fault diagnosis that above systems can not meet very well mainly in the following aspects [7, 8, 9]: Most systems are not mature from a production point of view and their reliability and stability are not satisfactory and need improvement. The function of the system seems a little simple. And the configuration of the function is not flexible. In addition, man-machine interface is not friendly. Thus, the operation of the system is not easy for the ordinary operating worker. The construction and the data format of the system lack openness. And thus, it is always difficult to connect the system to the production management network of the plant. This situation limits the usage of the system. The diagnosis mode and reasoning strategy of the diagnosis sub-system are always unitary. And thus the reliability and accuracy of the diagnosis result are not satisfactory. The function of Internet based remote monitoring and diagnosis is still at an initial stage. For most systems, so called Internet based remote monitoring and diagnosis just means data remote delivery and communication. With respect to above problems, in this paper, an Internet/Intranet based remote condition monitoring and fault diagnosis scheme is proposed. The corresponding technique and methods are discussed in detail. And a real application system is developed for the 300MW steam turboset. In this scheme, the system is built on the Internet/Intranet and the Client/Server construction and Web/Server model are adopted to guarantee real implementation of remote condition monitoring and fault diagnosis. The system includes four subsystems: Data sampling subsystem, real-time monitoring sub, data and Web server subsystem and fault analysis and diagnosis subsystem. Each subsystem is responsible for corresponding task. And such distributive system construction can guarantee the on-line ability and real time ability of the system simultaneously. The paper is organized as follows: In section 2, a general scheme design of the system is discussed, including function determination, hardware platform and software environment, etc. Section 3 gives a description of the various subsystems in detail. Section 4 introduces the real application system for the 300MW steam turboset. Finally, Section 5 summarizes the conclusions of the research in this paper. General Schematic Design of System Design Principle and Function Determination. Being different from the ordinary instruments and meters, through practical and effective function determination and open construction, the condition monitoring and fault diagnosis system realizes multiple dimensional monitoring of the running condition, finds out the trend of the running condition, diagnoses the extent, the cause and the location of the faults and finally serves the equipment management and the condition based maintenance strategy. Thus, the scheme design and function determination of the system will follow the following principles: Openness in the construction and compatibility in the hardware and software for the future extension of the system and connecting to other systems. Internet/Intranet based system construction for the remote monitoring and diagnosis. Reliability and stability in the hardware and software for long-term on-line monitoring. Combining automatic diagnosis and manual diagnosis for the practicability and flexibility of the diagnosis function.
Key Engineering Materials Vols. 293-294 367 Friendly man-machine interface for convenient usage. Function determination should guarantee the following practical functions: Shutdown protection, fault pre-warning, fault diagnosis, running optimization, performance evaluation, maintenance instruction, maintenance evaluation and decision consultation and equipment management. General Scheme and Construction. Based on the above principles, Internet/Intranet based network construction is adopted and the general scheme and construction of the system is as shown in fig.1. Host server of LAN User terminal 1 User terminal N Condition data server Condition data Web server Remote user LAN Internet/Intranet Router Host server of LAN User termina l User terminal N Condition data Web server Router LAN of plant (TCP/IP) Feature data Condition data server Original data Field monitoring station of the system Data convertor DCS system Fig. 1. The general scheme and construction of system. The system adopts Client/Server construction and Web/Server mode. The filed monitoring station is responsible for collecting all the original data, pre-processing data, arranging data according to some format and then delivering processed data to the conditioned data server. The condition data server is responsible for storing and managing all kinds of data. And the Web server is in charge of releasing all such data to the Internet/Intranet, including the information exchange, communication and sharing with local and remote fault analysis and diagnosis terminals and other systems. Hardware Platform and Software Environment of System. Hardware Platform: To guarantee the function realization of the system, the configuration of the hardware is determined as in fig.2. The system is supported by such a hardware platform. Software Environment: Operating system: The system adopts Windows NT 4.0 or Windows 2000 as its operating system. Software for data server: The data server adopts Oracle 8 for the data base. As a solving scheme for the opening system, Oracle 8 is very suitable for the interrogation and processing of the large-scale data base, and managing and processing the distributed data on the Internet.
368 Damage Assessment of Structures VI Software for Web server: Microsoft IIS is adopted in the Web server, which integrates WWW service, FTP service and Gopher service and supports CGI (Common Gateway Interface) and offers IDC (Internet Database Connecter) based ISAPI (Internet Server API). Network protocol: TCP/IP protocol is adopted in the system. Data base interface: ODBC (Open Database Connectivity) interface is adopted, on while the application program developed is of good expansibility and compatibility. Client software: Client terminal uses Microsoft IE to browse the data and monitoring page. Fig. 2. The hardware configuration of system. Developing tools of the application software: Microsoft Visual C++ 6.0 is used to develop the various function software, such as data sampling software, data pro-processing software, analysis and diagnosis software, etc. Microsoft FrontPage is utilized to produce the network page in the Web server. Function Description of Subsystem According to the function determination of the system, the system adopts distributive construction and is divided into four subsystems: Data sampling subsystem, real-time monitoring subsystem, data and Web server subsystem and fault analysis and diagnosis subsystem. Each subsystem is responsible for its corresponding task. Data Sampling Subsystem. This subsystem is responsible for collecting all the condition data of the steam turboset. According to the demand of the condition monitoring and fault diagnosis, the following kinds of condition data need to be acquired, which are vibration of the shaft (14 channels), vibration of the bearing bush (14 channels), rotating speed (1 channel), axial displacement of the shaft (1 channel), differential expansion of the set (3 channels), expansion of the shell (1 channel), eccentricity of the shaft (1 channel), pressure of lubricant oil (3 channels), temperature of the thrust pad (2 channels), pressure and temperature of the steam (6 channels), vacuum condenser (1 channel). The subsystem mainly includes the preprocessing unit, data sampling unit, sample control unit and filed bus control unit. The function structure and data flow of the subsystem are as shown in fig.3.
Key Engineering Materials Vols. 293-294 369 Photosensor Vibration signal Displacement sensor Velocity sensor Preprocessing Velocity signal Sample control Data store Field bus control Field bus Secondary meter Other signal Fig. 3. The structure and data flow of data sampling subsystem. Real-time Monitoring Subsystem. This subsystem mainly executes reorganizing and preprocessing of data from the data sampling subsystem, and delivers this reorganized Condition judging/ Warning and preprocessed data to the data server, and in the Time or freq. wave meantime, performs real-time condition monitoring. Data preprocessing includes FFT Axis trace/ Bar computation, interpolation, Network equi-phase-angle sampling, Data store control LAN peak-peak value computation, feature extraction and Fig. 4. The structure and data flow of real-time monitoring subsystem. statistical computation, etc. Real-time monitoring contents includes time / frequency domain analysis, envelope analysis, waterfall spectrum figures, cluster analysis, axis trace, bar figure, trend charts and condition recognition and warning, etc. The function structure and data flow of the subsystem are shown in fig.4. Field bus Data and Web Server Subsystem. Data and Web server is the main data storage device of the system, which is the centre of data communication and exchange and also the crucial unit for guaranteeing expansion and compatibility of the system. Via this unit, the system can easily be connected to other systems (such as DCS and MIS) as long as these systems offer their data interface. The function structure and data flow of the subsystem are shown in fig.5. Communication control Data management and processing Internet/Intranet Data acquisition and processing Fault Analysis and Diagnosis Subsystem. This subsystem obtains relevant data from the data server through the Web server and uses obtained data to execute detailed condition analysis and fault diagnosis, which mainly includes a parameter editing unit, a condition analysis unit, a fault Credence verification Network service management and control CRT Windows NT/Windows 2000 (Parallel multi-task network operation system) Data base creation, verification and inquiry Server configuration Local data server Web server Other Fig. 5. The structure and data flow of data and Web server
370 Damage Assessment of Structures VI diagnosis unit and a network control unit. The parameter editing unit edits relevant parameters of the system, such as sampling parameters, calibration parameters of the sensor, warning thresholds, etc. The condition analysis unit offers comprehensive analysis methods including time domain methods, frequency domain methods time-frequency domain methods, steady-state analysis methods and speed-up-down analysis, etc. The fault diagnosis unit integrates symbol based inference and neural network based inference to improve inference accuracy and efficiency. To improve diagnosis accuracy and reliability, some machine learning method is used to deal with knowledge acquisition and updating of the knowledge base. The network control unit is responsible for data communication and exchange between this subsystem and the data and Web server subsystem. The function structure and data flow of the subsystem are shown in fig.6. Fault Diagnosis Expert System [3, 10]. The diagnosis expert system is a hybrid expert which is Local data server Web server Network management and Fault diagnosis Condition analysis Symbol based inference Numerical inference Time series analysis pack Knowledge base creation and management Neural network design Neural network analysis pack Network training Wavelet analysis pack Inference Data base Spectral analysis pack Diagnosis and rule extraction Man-machine interface Other analysis C R T 界面 Fig. 6. The structure and data flow of fault analysis and diagnosis subsystem. based on rules, object frames and models. In the system, Figure and image recognition techniques and neural network techniques are utilized to intelligently recognize and automatically acquire the running symptoms of the set. Based on knowledge rules and the fault object frame, the system generates a fault tree for inference automatically or manually. The system combines rule-based knowledge and model-based knowledge and uses the former to diagnose the occurrence of the fault and the latter the location and extent of the fault. Based on neural networks and genetic algorithms, a diagnosis case learning unit is developed to perfect and update the knowledge base. The block diagram of the diagnosis expert system is shown in fig.7. Application System for 300MW Steam Turboset Based on the above scheme, an application system of condition monitoring and fault diagnosis is developed for the 300MW steam turboset of Guangdong Huangpu Electric Power Plant in China. The application system was installed in the plant in March last year. And up to now, the system works very well and provides much valuable information of the turboset to the plant, which is very helpful for the plant to efficiently use, manage and maintain this key equipment. Especially, the system guarantees the safe running of the set and avoids serious accidents. Because of such benefits, the system obtains more and more recognition and welcome from the plant and was awarded the
Key Engineering Materials Vols. 293-294 371 Science Technology Advancement Award by the Guangdong province in China. Fig.8 and fig.9 are examples of two kinds of interface in the system. Data base management Data acquisition from data server Network control Data preprocessing Data Knowledge base Knowledge base management Auto-acquisition of symptom Manu-acquisition of symptom Service Inference machine Dynamic data base Model based diagnosis Auto-diagnosis based on neural networks Diagnosis case learning Task management (Manu and interface) Fig.7 The block diagram of fault diagnosis system Fig. 8. Main monitoring. Fig. 9. Axis trace analysis interface. Conclusion The steam turboset is the key equipment of the power system. Safe and efficient running of it guarantees the production of the power plant. Therefore, it is very important and necessary to monitor and diagnose the running condition and the faults of the steam turboset. In this paper, the Internet/Intranet based remote condition monitoring and fault diagnosis scheme is proposed. The corresponding technique and methods are discussed in detail. And a real application system is developed for the 300MW steam turboset. In this scheme, the system is built on the Internet/Intranet and the Client/Server construction and Web/Server model are adopted. The system includes four subsystems: Data acquisition subsystem, data preprocessing subsystem, data and Web server subsystem and fault analysis and diagnosis subsystem. The data acquisition
372 Damage Assessment of Structures VI subsystem is responsible for collecting all the original condition data of the turboset and passing it to the data preprocessing subsystem. Such original data is preprocessed in the time and frequency domain respectively and arranged according to some format and then delivered to the server subsystem by the data preprocessing subsystem. In the server system, the data server is responsible for storing and managing all kinds of data. And the Web server is in charge of releasing all such data in the data server to the Internet/Intranet, including information exchange, communication and sharing with local and remote fault analysis and diagnosis terminals and other systems. The developed application system for the 300MW steam turboset has been installed in the plant and has proven good performance. It has brought much benefit to the plant and obtained recognition and award from the plant. Reference [1] Z. N. Fang, F. L. Chu: Journal of Tsinghua University Vol. 36 (70) (1996), p. 42-48. [2] S. Z. Yang, T. L. Shi, et al.: Journal of Vibration, Measurement & Diagnosis Vol. 17(1) (1997), p. 1-6. [3] Y. Y. He, X. J. Zhang, et al.: Chinese Power Vol. 29(3) (1996), p. 20-24. [4] M. P. Jia, F. Y. Xu, et al.: Proceedings of the CSEE Vol. 18(1) (1998), p. 60-63. [5] W. D. Ni, D. X. Jiang, et al.: Journal of Tsinghua University Vol. 37(S1) (1997), p. 1-3. [6] T. Elliott: Power Vol. 133(12) (1989), p. 53-55. [7] J. Wang, Q. Wang, et al.: Turbine Technology Vol. 46(1) (2004), p. 1-4. [8] A. Mukherji: Proc. 11 th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM'98) Vol.2 (1998), p. 627-632. [9] Internet-based Next Generation Remote Diagnostics, School of Engineering, Stanford University, http://diagnostics.stanford.edu/overview.html [10] W. Xue, Y. Shuzi: Artificial Intelligence in Engineering Vol. 10(4) (1996), p. 335-341.