Open AccessDetection of Spike-type Stall of Axial Compressors Based on Dilated Causal Convolutional Neural Networks
Author(s) -
Hongyang Zhao,
Fuxiang Quan,
Xia Weiguo,
XiMing Sun
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012028
Subject(s) - stall (fluid mechanics) , axial compressor , computer science , spike (software development) , time domain , gas compressor , convolutional neural network , anomaly detection , pattern recognition (psychology) , artificial intelligence , control theory (sociology) , engineering , computer vision , aerospace engineering , software engineering , control (management)
An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as spike-type stall inception in axial compressors is observed in aero-engine. In this paper, a deep dilated causal convolutional neural network(CNN) named as WaveNet is applied to spike-type stall inception detection and prediction in time-series data of axial compressors. WaveNet can implement fast anomaly detection and spike-type stall prediction in long-time term series data. Furthermore, a single WaveNet can be trained to capture and learn the time-domain statistical characteristics of different spike-type stall inception training data with equal fidelity. The trained WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of spike-type stall detection approach based on WaveNet.