Yong Yong He

Internet/Intranet Based Remote Condition Monitoring and Fault Diagnosis Scheme and System for Steam Turboset

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.

A Steam Turbine Fault Diagnostic System Based on the Web and Database Technologies

The steam turbine is the principal mover in the field of electric power generation. It is very important and necessary to monitor and evaluate the running conditions and diagnose the faults of the steam turbine for the safe and normal running of the electric power plant. Fault diagnosis of the steam turbine is a complicated process and requires high level of the expertise. In order to significantly reduce the cost consumed in the fault diagnosis, increase the consistency of diagnosing decision-making, and better utilize the turbine’s management information resource, in this paper, an integrated Web-based expert system of the fault diagnosis for general purpose has been developed for the steam turbine of a power station. Moreover, it can be regarded as an advisory tool to those field engineers having much technical experience and as a training tool to less-experienced personnel who need guidance and advice. This paper describes a research project aiming to develop a web-based intelligent diagnostic system for the steam turbine, and discusses the process of the fault diagnosis and the issues involved in developing the system. The paper also includes several practical issues related to the architectures of the intelligent web-based applications. The system is built on a three-tier architecture, including the following components: knowledge base, inference engine, knowledge administration interface, user interface, knowledge administration, and integrated database. The diagnostic system employs heuristic rules to diagnose the steam turbine faults.

Smart Rotating Machines for Condition Monitoring

The concept that changes in the dynamic behaviour of a rotor could be used for general fault detection and monitoring is well established. Current methods rely on the response of the machine to unbalance excitation during run-up, run-down or normal operation, and are mainly based on pattern recognition approaches. Of all machine faults, probably cracks in the rotor pose the greatest danger and research in crack detection has been ongoing for the past 30 years. For large unbalance forces the crack will remain permanently open and the rotor is then asymmetric, which can lead to stability problems. If the static deflection of the rotor due to gravity is large then the crack opens and closes due to the rotation of the shaft (a breathing crack), producing a parametrically excited dynamical system. Although monitoring the unbalance response of rotors is able to detect the presence of a crack, often the method is relatively insensitive, and the crack must be large before it can be robustly detected. Recently methods to enhance the quality of the information obtained from a machine have been attempted, by using additional excitation, for example from active magnetic bearings. This research is directed towards the concept of a smart rotating machine, where the machine is able to detect and diagnose faults and take action automatically, without any human intervention. This paper will consider progress to date in this area, with examples, and consider the prospects for future development.

Experimental Study on Rubbing Acoustic Emission for Rotor-Bearing System Based on Parameter Analysis

Rubbing between the rotor and the stator is the frequent fault and will cause very serious accident, even catastrophe, to rotating machinery. Therefore, timely detection of such rubbing is very important avoid severe consequences. Vibration based methods are very mature and traditional methods but not effective to detect incipient rubbing in rotating machinery. Rubbing between the rotor and the stator will cause elastic strain in the rubbing location and thus can produce acoustic emission (AE). Apparently, such AE contains direct and abundant information about the rubbing and can be used to detect and diagnose such fault effectively. In this paper, the AE based method is proposed for detecting and identifying the rubbing of the rotor-bearing system. An experimental study is presented to investigate the characteristics and features of rubbing AE using parameter analysis method. The results show that AE is very sensitive to the rubbing and AE parameters can reveal the characteristics and features of rubbing AE. Parameter analysis demonstrates that rubbing AE contains multiple modals, which will attenuate with propagation by negative exponent manner.

Fault Detection in Rotor-Bearing System Using Measured Vibration Data

A general method for detecting fault of rotor-bearing system, by using of measured vibration data, is proposed and described in this paper. It formulates the fault detection as an inverse problem and the optimized method is used to solve it. The presented met hod needs merely on-line measured vibration data of several monitor points but not of whole nodes of r otor, and the machine need not to be stopped. This method can be applied to detect the multi-fault s of rotor system. Several experiments to detect depth and location of shaft transve se crack, size and angle of disk mass eccentricity and oil-film coefficients of rotor-bearing system have been implemented to illustrate the method. Introduction How to detect rotor faults, such as the size and location of shaft crack, the magnitude and position of mass eccentricity, the location of rub fault etc., has become inc r asingly an important research topic in connection with safety evaluation and damage assessment of rotor machine. Since the vary of parameters in a structure modifies the vibrati on behaviour of the structure, a strong interest has been developed within the last 20 years about the fa ul identification by means of the knowledge of vibrational behaviour of a structure. Among them, the model-based faul t detection method is one of the most important quantitative approaches. The modelbased method uses a simulated model of system or of the process to obtain the relation between output and fault parameters(fault type, location and severity). Among recent contribut ions in this field, Jiang,etc[1], Chen,etc[2], Zhang, etc.[3] reported their investigations for oil-film dynamic coefficients estimating by use of synchronous response or frequency response function of rotor sy stem. Markert etc.[4] and Platz etc. [5] present a model to generate a dynamic behaviour identical to the measured one of the damaged system. Krodkiewski,etc.[6], Shin,ect.[7], Edwards,etc.[8] prese nted model-based methods to determinate the configuration or unbalance of rotor system . A ore comprehensive approach has been introduced by Bachschmid and Pennacchi [9] to identify s veral different types of faults. Their method has been experimentally validated on different test-rigs and real machines Key Engineering Materials Online: 2003-07-15 ISSN: 1662-9795, Vols. 245-246, pp 527-536 doi:10.4028/www.scientific.net/KEM.245-246.527