# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection
### Vibration Signals
#### 3.2.2 Processing of Vibration Signals  
Numerous studies have looked at new processing techniques for the signals from vibration-based knock sensors, with a primary goal of increasing the accuracy of knock determination. [PERSON] et al. [32] proposed a novel knock recognition method that uses a combustion model to estimate the mass fraction that is burned when vibrations are sensed by a knock sensor. The amplitude of the vibration wave depends on the amount of fuel and air that autoignite. The threshold values for the vibration sectors to indicate that a cycle is knocking were then modified to reflect when in the cycle the vibrations were detected. Vibrations detected late in the cycle had a lower threshold than ones earlier in the expansion stroke when there was a larger mass of unburned fuel in the cylinder. It is important to note that the amount of non-knock vibrations for an engine follows a similar pattern, since there are considerably more vibrations at the top of the expansion stroke than later in the stroke. Compared to the commonly used fixed threshold approach, the proposed method demonstrated a significant improvement of over 10% in knock detection accuracy, as validated against cylinder pressure data. While promising, such an approach could be difficult to implement given the need for combustion models for a range of operating conditions. Meanwhile, [PERSON] et al. [33] proposed a knock recognition method using wavelet transform and variational mode decomposition to process knock sensor signals. This approach, similar to how pressure data are processed, is more precise than bandpass filtering, which maintains a fairly wide range of frequencies. Rather, the approach by [PERSON] et al. [33] allowed the engine control unit to focus on a narrow set of frequencies, allowing for decreased noise and increasing the ability to extract knock features. The proposed method increased the accuracy of knock measurements compared to traditional bandpass filtering, but also required increased processing. Another study, performed by [PERSON] et al. [34], also performed a spectral analysis of the vibration signal to determine the frequencies of interest associated with knock. The study then proceeded to use genetic algorithms to expand the range of frequencies that are considered for detecting knock, setting the appropriate threshold values for determining the onset of knock. The signals were then reconstructed using only the frequencies of interest; the denoised signals were then trained using a sparse Bayesian extreme learning machine. While this multi-step algorithm is much more complex than what is commonly used, [PERSON] et al. [34] were able to achieve a classification accuracy of 98.3 percent, which is significantly higher than conventional processing techniques. Another method was developed by [PERSON] et al. [35], who used machine learning with a similar Empirical Mode Decomposition method. This study introduced a quantum threshold method to reduce the signal noise in the vibration sensor. The model was trained by comparing vibration data to pressure data to identify knocking cycles. The approach by [PERSON] et al. [35] then actively varied the filter parameters of the vibration data to reduce the engine noise. The study found that this technique enhanced the detection accuracy compared to previous wavelet transform methods. Their models showed a 3.4% improvement in accuracy for simulated signals and a 13.2% improvement in actual signals, emphasizing the effectiveness of their approach. However, similar to the other studies, it does require the engine control unit to have significantly higher processing capabilities. [PERSON] et al. [36] used both supervised and unsupervised ML approaches to process vibration signals to detect knock in heavy-duty engines. The evaluated methods encompass a One-Class Support Vector Machine and Convolutional Neural Networks.