Technical Paper
Diesel Engine Acoustic Emission Fuel Cetane Diagnostics with Machine Learning
2019-04-02
2019-01-0555
A diesel engine electrical generator set (‘gen-set’) was instrumented with in-cylinder pressure indicating sensors as well as a nearby microphone. Conventional jet fuel plus high (Cetane Number CN55) and low (CN35) secondary reference fuels were operated during which comprehensive engine and acoustic data were collected. Fast Fourier Transforms (FFTs) were analyzed on the acoustic data. FFT peaks were then applied to machine learning neural network analysis with MATLAB based tools. Detection of the low and high cetane fuel operation was audibly determined with correlation coefficients greater than 98% on test data sets. Further, unsupervised machine learning Self Organizing Maps (SOMs) were produced during normal-baseline operation of the engine with jet fuel. Application of the high and low cetane fuel operational acoustic data was then applied to the normal SOM.
