Abstract:The spark plug of an opposed-piston two-stroke (OP2S) gasoline engine is arranged on the side wall of the cylinder liner, far from the center of the combustion chamber; the ignition core of the mixture is offset, the flame propagation distance is increased, the combustion duration is prolonged, and the knock tendency is severe. In this paper, a quasi-dimensional two-zone combustion model is used in GT-Power software to establish a thermodynamic process simulation model and a knock prediction model is included … Show more
“…Abnormal ignition advance angle will not only reduce the engine performance and deteriorate the fuel economy, but also lead to strong engine detonation, which is a threat to the normal operation of the engine, thus posing a great safety hazard. Figure 1 Sectional view of the internal structure of the engine 2 . …”
Section: Introductionmentioning
confidence: 99%
“…Not only that, many studies have shown that the ignition advance angle has a great impact on engine knock 7 – 9 . Ma et al found that with the increase of the ignition advance angle, the pressure and temperature in the cylinder rise rapidly, which increases the possibility of engine knock combustion 2 . Although a slight knock is beneficial to the performance of the engine, if it is uncontrolled and develops into a strong knock, it will reduce the performance of the engine and even cause serious damage to the engine structure.…”
Ignition advance angle is one of the important factors affecting the performance of the engine, when it occurs abnormally will make the engine power and economy worse, and even cause serious damage to the engine. Therefore, it is very necessary to recognize the abnormal ignition advance angle of the engine. However, the engine system is closed and has a complex structure, which makes traditional diagnostic methods difficult. This paper proposes an intelligent identification method based on acoustic emission (AE) signals, which collects the AE signals from the engine surface and divides their spectra into equal parts, and selects the frequency bands with high contribution to the classification based on the minimum distance method to construct feature maps, which is used as the input to the convolutional neural network (CNN). The extracted frequency band features of this method can better characterize the AE signals, and the constructed feature maps make the fault information more obvious. Experiments show that the accuracy of this method for abnormal ignition advance angle under normal operating conditions of piston aero-engine is 100%, which is better than the traditional methods. In addition, the recognition accuracies under the other two operating conditions are 99.75% and 98.5%, respectively, indicating that the method has a certain universality.
“…Abnormal ignition advance angle will not only reduce the engine performance and deteriorate the fuel economy, but also lead to strong engine detonation, which is a threat to the normal operation of the engine, thus posing a great safety hazard. Figure 1 Sectional view of the internal structure of the engine 2 . …”
Section: Introductionmentioning
confidence: 99%
“…Not only that, many studies have shown that the ignition advance angle has a great impact on engine knock 7 – 9 . Ma et al found that with the increase of the ignition advance angle, the pressure and temperature in the cylinder rise rapidly, which increases the possibility of engine knock combustion 2 . Although a slight knock is beneficial to the performance of the engine, if it is uncontrolled and develops into a strong knock, it will reduce the performance of the engine and even cause serious damage to the engine structure.…”
Ignition advance angle is one of the important factors affecting the performance of the engine, when it occurs abnormally will make the engine power and economy worse, and even cause serious damage to the engine. Therefore, it is very necessary to recognize the abnormal ignition advance angle of the engine. However, the engine system is closed and has a complex structure, which makes traditional diagnostic methods difficult. This paper proposes an intelligent identification method based on acoustic emission (AE) signals, which collects the AE signals from the engine surface and divides their spectra into equal parts, and selects the frequency bands with high contribution to the classification based on the minimum distance method to construct feature maps, which is used as the input to the convolutional neural network (CNN). The extracted frequency band features of this method can better characterize the AE signals, and the constructed feature maps make the fault information more obvious. Experiments show that the accuracy of this method for abnormal ignition advance angle under normal operating conditions of piston aero-engine is 100%, which is better than the traditional methods. In addition, the recognition accuracies under the other two operating conditions are 99.75% and 98.5%, respectively, indicating that the method has a certain universality.
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