# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Pressure Signals
#### 3.1.2 Processing of Pressure Signals  
For both the lab setting and potential use on roads, it is necessary to advance the algorithms for detecting knock from pressure signals, especially at borderline knock conditions. Although knock pressure waves are characterized by pressure oscillations at set frequencies, the exact frequencies and amplitudes are dependent on a number of parameters and vary from cycle to cycle. Figure 4: Summary of current and new advances in signal acquisition and processing of pressure data for the detection of knock. Several studies have identified modified methods to calculate the power spectrum density calculations which capture the frequency contributions of pressure oscillations associated with knock. [PERSON] et al. (2019) improved the detection of knock in standard gasoline engines by analyzing a narrow window around 20 crank angle degrees after the expected start of knock combustion. During this window, the raw pressure signal is less noisy, resulting in an increased ability to differentiate the oscillations associated with knock. However, in borderline knock cases, the pressure oscillations associated with knock in this window have a very low amplitude and are difficult to detect. Meanwhile, [PERSON] et al. (2019) found that pressure signals should be sampled at a higher frequency rate in order to be able to detect the higher frequencies that play a more prominent role in knock in modern boosted engines. This technique requires a faster response pressure transducer and increased processing capabilities, but better captures high frequency oscillations. [PERSON] et al. (2019) presented a novel knock detection approach using in-cylinder pressure principal component analysis, which moved away from traditional bandpass filtering. The study introduced \"eigenpressures\", which effectively allowed for the modeling of an engine cycle without the oscillations associated with knock. The \"eigenpressures\" are based on a singular value decomposition based on experimental pressure traces. Their technique was then able to isolate the oscillations associated with knock. The proposed method outperformed standard techniques, including bandpass filtering, exhibiting better knock classification performance with few easily tunable parameters. While promising, such an approach may be difficult when considering the broad range of engine operating conditions that are associated with knock. Several studies introduced more advanced processing techniques to detect knock signals, especially at borderline knock conditions, when the pressure oscillations have a low amplitude. A study by [PERSON] (2017) developed a machine learning model using in-cylinder pressure data to distinguish knock from normal cycles. The model presented in the study used Wavelet Packet Decomposition and Ensemble Empirical Mode Decomposition, which effectively identify the primary oscillation modes of the pressure signals and decompose the pressure signal into its frequency components. The algorithm is able to adjust to different engine operating conditions and identifies the relevant frequencies for detecting knock. This approach, as outlined in the study, allowed for a higher level of accuracy in detecting knock at borderline knock conditions while requiring less processing. [PERSON] et al. (2019) conducted research on predicting knock occurrences using deep learning with the in-cylinder pressure history. Deep learning involves using neural networks to better identify complex patterns, which are then used to train a model. In this case, the form of the pressure trace prior to the onset of knock could be used to predict whether or not knock would occur. The study concluded that deep learning is effective in predicting knock occurrences using the in-cylinder pressure history, even across multiple operating conditions. [PERSON] et al. (2019) performed a similar study that used a 1D convoluted neural network trained on pressure data.