Detection of Knocking Combustion Using the Continuous Wavelet Transformation and a Convolutional Neural Network

Detection of Knocking Combustion Using the Continuous Wavelet Transformation and a Convolutional Neural Network

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The phenomenon of knock is an abnormal combustion occurring in spark-ignition (SI) engines and forms a barrier that prevents an increase in thermal efficiency while simultaneously reducing CO2 emissions.Since knocking combustion is highly stochastic, a cyclic analysis of in-cylinder pressure is necessary.In this study we propose an approach for efficient and robust detection and identification of knocking combustion in three different internal combustion engines.The proposed methodology includes a signal processing technique, called continuous wavelet transformation (CWT), which provides a simultaneous analysis of the in-cylinder pressure traces in the time and frequency domains with coefficients.These coefficients serve as input for a convolutional neural network (CNN) ultimate warrior hat which extracts distinctive features and performs an image recognition task in order to distinguish between non-knock and knock.

The results revealed the following: (i) The CWT delivered a stable and effective feature space with the coefficients that represents the unique time-frequency pattern of each individual in-cylinder pressure cycle; (ii) the proposed approach was superior to the state-of-the-art threshold value exceeded (TVE) method with a 9.5-4 igora vibrance maximum amplitude pressure oscillation (MAPO) criterion improving the overall accuracy by 6.15 percentage points (up to 92.62%); and (iii) The CWT + CNN method does not require calibrating threshold values for different engines or operating conditions as long as enough and diverse data is used to train the neural network.