Abstract
Stall warning of axial compressor is very challenging and the existing warning margin is not enough. A algorithm based on BP neural network fusion fuzzy logic is proposed. Firstly, BP neural network is used for training recognition, next the identification results are fused with fuzzy logic reasoning to form the result judgment of time sequence, finally the stall early warning of axial compressor is realized. The simulation results of the experimental data show that the stall data at all speeds are at least 0.1s in advance of the early warning. Compared with other methods, this method has a better surge early warning margin performance and engineering practicability.
Highlights
The rotating stall of the compressor can lead to surging, resulting in broken blades and damage to the internal structure of the compressor, [1] which in turn affects the safety and performance of the compressor, and even reduces the economic benefits of the compressor
The neural network has the advantage of strong characteristics learning ability, and it is seldom used in the research of compressor stall early warning
The output result of the BP neural network needs to be accumulated in time series, and further processed by fuzzy logic control to reduce the misjudgment of stall warning, to improve the correct rate of compressor stall warning
Summary
The rotating stall of the compressor can lead to surging, resulting in broken blades and damage to the internal structure of the compressor, [1] which in turn affects the safety and performance of the compressor, and even reduces the economic benefits of the compressor. The stall characteristics of the compressor are discussed, and the stall detection method is explored. Researches can detect stall signals in experiments, but these methods have not been widely used in practical engineering. Only Methling [18] discussed the possibility of using Artificial Neural Network method to identify the initial surge stall detection of compressor, but it is still difficult to meet the early warning requirements of compressor stall. Lin Peng [19] used neural network model to detect the precursor characteristics of compressor distortion stall. The neural network has the advantage of strong characteristics learning ability, and it is seldom used in the research of compressor stall early warning. By verifying the experimental data of different speed modes, the feasibility and practicability of the algorithm are confirmed, which is beneficial to improve the warning margin and engineering practical value
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