Machine learning based technique for outlier detection and result prediction in combustion diagnostics

Abstract

With the utilization of data correlation, this work proposed a novel machine learning-based technique for outlier detection and result prediction to cover the deficiency and economize the cost of current combustion diagnostic technology. For outlier detection, the measurement results were performed through cluster analysis. Results demonstrated the measurement outlier could be successfully detected and eliminated by DBSCAN and the proposed GBCN algorithm. For result prediction, an artificial neural network (ANN) was established based on the correlation of the measured data, and its performance was investigated by the statistical method. Results indicated the ANN could well “learn” the distribution characteristic of flame temperature with a high value of R and a low value of MAPE by selecting Tanh and Sigmoid as activation functions. When there were 30 % and 50 % continuous unmeasured temperature data inside the flame, the ANN could still predict their values approximately based on the correlation between the remaining data. With the increase of prediction amount to 60 %, the prediction performance decreased significantly. However, even if there were 70 % random unmeasured temperature data inside the flame, the whole temperature field could still be obtained by the prediction of ANN with high accuracy based on the correlation between a small amount of measured data.