Detection of blade contamination in turbine flowmeters using neural networks
نویسندگان
چکیده
The accumulation of contaminating materials on the rotor blades of a gas turbine flowmeter is a condition of concern to the manufacturers, since it can lead to loss of calibration. Over time, a deposit of tar and heavy oil mixed with sand and dust can build up on the rotor blades, which causes the meter factor to change from its original value of calibration. A novel neural-network technique is described for the detection of blade contamination. The method uses a C language implementation of the multi-layer perceptron (MLP) neural network, which uses a cumulative backpropagation (BKP) algorithm to train and be tested on blade passing time vectors (BPT).
منابع مشابه
Robust Fault Detection on Boiler-turbine Unit Actuators Using Dynamic Neural Networks
Due to the important role of the boiler-turbine units in industries and electricity generation, it is important to diagnose different types of faults in different parts of boiler-turbine system. Different parts of a boiler-turbine system like the sensor or actuator or plant can be affected by various types of faults. In this paper, the effects of the occurrence of faults on the actuators are in...
متن کاملNew Indices for Detection of Turbine blade Tip Deformation and Estimation of Clearance Extent Using Scattering Parameter
Reliability of gas turbine power plant is related to the blade normal operation. In this paper, a new on-line condition monitoring method has been presented to evaluate two important factors affecting the turbine blades that are the tip clearance and tip deformation. A K band’s microwave sensor has been simulated and optimized in CST Microwave Studio software. Scattering parameters of the near...
متن کاملNeural network condition monitoring of a turbine flowmeter
A novel neural-network based technique is described for the remote condition-monitoring of an in-service gas-turbine flowmeter. The method uses a C language implementation of a modified multi-layer perceptron (MLP) neural networks, which enables detection of the accumulation of contaminating material on the rotor blades that could lead to changes in meter-factor and loss of calibration.
متن کاملProbabilistic Contaminant Source Identification in Water Distribution Infrastructure Systems
Large water distribution systems can be highly vulnerable to penetration of contaminant factors caused by different means including deliberate contamination injections. As contaminants quickly spread into a water distribution network, rapid characterization of the pollution source has a high measure of importance for early warning assessment and disaster management. In this paper, a methodology...
متن کاملImproving Data-based Wind Turbine Using Measured Data Foggy Method
The purpose of this paper is to improve the modeling of the data-driven wind turbine system that receives data from noise signals. Most of the data on industrial systems is noisely and data noise is inevitable and natural. The method and idea proposed in this paper, Data Fogging, significantly reduce the impact of noise on data-driven wind turbine system modeling, which is the basis of this met...
متن کامل