Papers by Keyword: Condition Monitoring

Abstract: The ageing level of ZnO materials in gapless surge arresters can be determined by using either the traditional leakage current measurements or recently introduced thermal images of the arrester. However, a direct correlation between arrester thermal images and its leakage current (and hence the ageing level) is yet to be established. This paper attempts to find such a correlation using an artificial neural network (ANN). Experimental work was carried out to capture both the thermal images and leakage current of 120kV rated polymeric housed gapless arresters. Critical parameters were then extracted from both the thermal image and the leakage current, before being exported into the artificial neural network tool. Using the leakage current level, the conditions of the arrester are classified as normal, caution, and faulty. The ANN correctly classifies the ageing level using only the thermal image information with an accuracy of 97%, which is highly encouraging.

Abstract: Surge arresters, as protective equipment, are used to limit any overvoltage in a power system resulting from various sources. The surge arrester affects from degradation due to the continuous operating voltage system as well as due to repeated lightning current discharge. Therefore, condition monitoring and health diagnostics of surge arresters are a necessary issue. Hence, as a feasible solution, a condition monitoring based on leakage current measurement techniques was selected to tackle the problem of age diagnostics of surge arresters. The Particle Swarm Search Algorithm was introduced as a method for extracting the third harmonic resistive component, Ir_3rd, from the total leakage current. To employ this method for extracting Ir_3rd, codes were developed in Matlab. The setting of Particle Swarm Search Algorithm was configured for extracting Ir_3rd and accuracy of 94% was obtained.

Abstract: Wind power becomes one of the most important cleaner energy in the world. The maintenance problem becomes the main factor preventing the appropriate power price which can be accepted by people. So, many researches contribute to improve the availability of wind farms. Traditionally, most researchers paid attention to the fault diagnosis, prognosis and maintenance strategies optimization to short the down time and the maintenance cost. But they all neglect the condition monitoring work of repaired gearbox of wind turbine before it is delivered to the customer from maintenance center to the wind farm. The life of repaired gearbox is also a very critical factor which influences the availability of wind farm. Good checking methods and good standard can provide a good support of good performance after the gearbox installed on the up tower. So, this paper proposed how to address these issues.

Abstract: Contrary to the difficult for complex equipment in the parallel signal acquisition, thereby affecting the equipment condition monitoring results, Design a parallel signal data acquisition device and its collection method based on embedded system. It can achieve real-time multi-point data collection and analyze parallel signal data acquisition system structure and function, then giving the hardware design of embedded systems based on the chip, including the processes of mass storage system and a plurality of communication method. Realize of the equipment condition monitoring and fault diagnosis based on this.

Abstract: The intelligent electronic device used for transformer multi condition monitoring platform was designed based on IEC61850 communication protocol. Based on the analysis of condition monitoring parameter, the overall program and module function were elaborated, the function and design method of signal conditioning module, data acquisition and transformer system, threshold setting were discussed in detail. The intelligent electronic device platform can realize comprehensive monitoring of multi state parameters of transformers. The monitored data will be uploaded automatically in case of the monitoring parameters overrunning. The platform is of a high degree of intelligence, good reliability, flexibility, versatility, and scalability.

Abstract: A methodology to detect multi crack, with crack location and size due to transverse loading is presented in this paper taking the advantage of Adaptive neuro-fuzzy inference system (ANFIS) using modal analysis of cracked shaft. A simple supported shaft is subjected with axial and bending load for same angular position along with its longitudinal direction. The natural frequency of the shaft with its various crack positions and size are accurately determined analytically using strain energy release rate and stress intensity factor. The vibration parameters such as first three relative natural frequencies with their mode shapes at different locations and size are supplied to ANFIS to optimize the results. The crack location and size predicted from ANFIS model are verified with the theoretical data with acceptable error. The present method used in this paper is suitable and can be easily extended to complex structure with different orientation of multiple cracks.

Abstract: Many researchers dealt with single fault but in real life situations, combined action of two or more single faults are usually present. Multi-faults (two and three combinations) were studied by few researchers. Researchers have mostly studied the vibration behavior of a rotor with misalignment, unbalance and rub separately. This paper presents one such case, where multiple faults are considered together in a rotor system, i.e. misalignment, unbalance and rub. The objective of this paper is to experimentally investigate the vibration response of combined of three faults misalignment, unbalance and rub using Fast Fourier Transform (FFT). FFT spectrum of combination of three faults misalignment, unbalance and rub show 1 X and 2 X are predominant and many sub harmonics are generated.1X peak represent unbalance, 2X peak represents misalignment and sub harmonics shows rub fault.

Abstract: A method for measuring the thickness of liquid-lubricated film of end face and detecting the friction of end face of mechanical seals has been presented in this article. Eddy current sensors installed on the inner ring embedded in the stationary ring of mechanical seals are used to measure the thickness of the liquid-lubricated film. Acoustic emission sensor placed on the stationary ring base is used to detect the friction of end face. The micro scope condition monitoring of end face is of importance to ensure mechanical seals run normally. With a set of tests, the results demonstrate that the method is effective.

Abstract: Establishing a test system model is necessary guarantee to realize the state monitoring, and also the premise of optimizing test points. The modeling method, principle and steps based on multi-signal flow model were analyzed in this paper. And a test point optimization method based on this model was studied. Through analysis and comparison, the proposed method can reduce complexity of monitoring system, avoid blindness of the diagnosis and reasoning process, and improve efficiency of fault diagnosis.

Abstract: Monitoring system of wind turbine is the guarantee of normal operation of the wind turbine. It has significance on the study, promotion and application of wind turbines. The research status of wind turbine monitoring system was mainly elaborated. The structure of wind turbine monitoring system was analyzed. The monitoring systems based on VB, VC, configuration software and LabVIEW have been made a comparison. And several further research directions of the wind turbine monitoring system have been proposed.