A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
1
Abstract. In this paper, aiming at the pressure fluctuation problem of high-pressure common rail system, based on the convolution neural network of WaveNet structure, a pressure fluctuation prediction algorithm is proposed. Compared with other pressure fluctuation algorithms, deep learning is applied in this algorithm. The effectiveness of the proposed algorithm is approved in the experiment in different target rated injection pressure, different injection interval and other working conditions. The results of the experiment show that the average accuracy of the prediction algorithm is about 99.1%, and meets the demand. The accuracy of the prediction the pressure fluctuation from the injection interval to the starting point of the main injection is higher, and the accuracy of the prediction of the pressure fluctuation and its attenuation process slightly decreases after the main injection duration.
Keywords: Prediction Algorithm, High Pressure Common Rail System, Pressure Fluctuation, CNN
1. INTRODUCTION Multiple injections technology in high pressure common
rail system can effectively optimize the combustion process,
reduce pollution emissions and reduce combustion noise [1].
The pressure fluctuation produced in the fuel injection
process of the high pressure common rail system has a
significant impact on the fuel injection quantity of the
system, which will seriously reduce the cycle consistency of
the fuel injection quantity of the system, and then have a
great impact on the power, economy and stability of the
engine [2]. Therefore, accurate prediction of pressure
fluctuation characteristics according to specific conditions
is the premise of accurate control of circulating oil quantity.
At present, most of the studies are based on numerical
model and simulation to predict the pressure fluctuation.
Ubertini et al [3] built a simplified simulation model of
high-pressure common rail system to simulate the real rail
pressure fluctuation. After parameter correction, it can
basically meet the prediction requirements. However, due to
the excessive simplification of the model, the generalization
ability of the simulation model is not enough, the model can
not accurately predict. In order to study the pressure
fluctuation in the high pressure common rail system,
Catalano et al [4] established a numerical model for
simulation. The research results show that the sharp drop of
pressure during fuel injection is caused by the acceleration
of fuel flow caused by the opening of fuel injection clock
valve. The numerical model only predicts the theoretical
fluctuation of fuel pressure, but not the actual situation, so it
has low practicability and generalization ability.
Compared with the traditional numerical model simulation
method, the deep learning method has unique advantages,
such as simple modeling, high precision and low
computational resource consumption [5]. Due to the lack of
research on the prediction of pressure fluctuation by using
deep learning method, this paper explores the prediction
algorithm of pressure fluctuation in high pressure common
rail system by using deep learning method, which is of great
significance for many problems like circulating oil control.
Convolution neural network can realize parallel input by
convolution sliding operation on input features. The
convolution neural network has fast training speed and low
computing resource consumption. In order to make use of
the advantages of convolutional neural network and
enhance its learning ability for time series information, this
paper uses WaveNet network structure to build prediction
model [6]. The prediction model of convolution neural
network is built by the hole causal convolution, residual
module and receptive field design in WaveNet.
In the experiment, the original data required by the
prediction model were collected through multiple injections
bench tests of high pressure common rail system. Based on
the structure of WaveNet, the pressure fluctuation
prediction model of high pressure common rail system is
established and a lot of comparative experiments are carried
out. The results of the experiment show that the average
accuracy of the prediction algorithm is about 99.1%. The
results show that the larger the receptive field is, the higher
the accuracy of the model is. Finally, the receptive field of
200 is used to predict the best effect.
In Section 2: the structure of algorithm is presented. In
Section 3: the experiment is presented and the experimental
results are analyzed.
2. THE STRUCTURE OF ALGORITHM CNN can implement parallel input by convolution sliding
operation on input. However, according to the basic
principle of CNN, ordinary convolution, pooling and other
Zhe Zuo*, Kuichen Quan, Meng Du
School of Mechanical Engineering, Beijing Institute of Technology,
No. 5 Zhongguancun South Street, Haidian District, Beijing 100081, China E-mail: [email protected]
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
2
operations are for two-dimensional or three-dimensional
input [5]. On the one hand, the injector inlet pressure,
driving current and injection rate obtained by bench test are
all time series information, belonging to one-dimensional
information; on the other hand, the ordinary convolution
operation in CNN is sensitive to the spatial position of
features in the calculation process, so it is unable to
effectively extract the time information of features.
Therefore, this paper refers to WaveNet structure, on the
one hand, the original convolution operation is converted
into one-dimensional convolution for operation on
time-series information; on the other hand, through the
design of the network structure, the ability of CNN to
extract the spatial information of input features is
transformed into the extraction ability of time information.
2.1. Causal Convolution The general convolution operation is calculated for
two-dimensional input. In order to model the sequence data,
a special convolution structure is needed. The causal
convolution is a one-dimensional convolution operation
designed for timing information. The basic principle of
causal convolution is shown in formula (1).
𝑝(𝑥) = ∏ 𝑝(𝑥𝑡|𝑥𝑡−𝑛, … , 𝑥𝑡−1)
𝑇
𝑡=1
(1)
The above formula represents the probability 𝑝(𝑥) of
obtaining the information of time step 𝑡 when the sequence
information (𝑥𝑡−𝑛, … , 𝑥𝑡−1) within 𝑡 − 𝑛 to 𝑡 − 1 is input.
The purpose of model training is to maximize the
probability of the real value 𝑥𝑡 of the model output, that is,
𝑝(𝑥) is as close as possible to 1. The basic principle of
causal revolution is shown in Fig. 1.
Fig. 1 Visualization of a stack of causal convolutional layers. The figure shows the forward process of causal
convolution calculation when the convolution kernel size is
2 and the step size is 1. The pink dot represents the input
timing information, the blue dot represents the output result,
the gray and yellow dots represent the intermediate result,
and the arrow represents the forward process direction of
the model. It can be seen from the figure that the prediction
output of the network for a certain time step is determined
by the connected input information. The size of the mapping
area between the output and input information of a node is
called receptive field. The size of receptive field in the
graph is 5. The size of receptive field is shown in formula
(2).
𝑓𝐿 = 𝑓𝐿−1 + (𝑘𝐿 − 1) ∏ 𝑠𝑙
𝐿
𝑙=1
(2)
where 𝑓𝐿 represents the size of receptive field of layer 𝐿, 𝑘𝐿
represents the size of 𝐿 -layer convolution kernel, 𝑠𝑙 is the
convolution step length of the first layer. Since the input
information is time-series information, in order to make the
prediction of time series more accurate, we need to increase
the receptive field as much as possible. For the prediction of
a certain time step, we hope that it can obtain the time series
information in the range as long as possible before the
current time step. One way to increase receptive field is to
increase the number of network layers, but increasing the
number of network layers will increase the burden of
network training and increase the tendency of over fitting;
on the other hand, the efficiency of increasing the number of
network layers is too low to increase the receptive field, so
dilated causal convolution is introduced.
2.2. Dilated Causal Convolution The design principle of dilated causal convolution is to
skip the input value at a specific position during convolution
operation, which is equivalent to expanding the size of
convolution kernel. The empty part in the middle of
convolution kernel is replaced by zero value. The number of
skipped features is controlled by division. The larger the
division is, the more feature points are skipped. The larger
the equivalent convolution kernel size is, the larger the
receptive field area can be obtained, as shown in Fig. 2.
Fig. 2 Visualization of a stack of dilated causal convolutional
layers. Compared with the method of increasing the number of
network layers, the efficiency of increasing receptive field
is greatly improved. The reduced caudal convolution can
keep the larger receptive field while limiting the number of
layers of the model network. Because the number of layers
of the network is small, the efficiency of the network in
training and prediction can still be maintained. In this paper,
in order to expand the receptive field, in the case of
appropriately increasing the number of network layers, this
paper adopts the diffused causal convolution, and gradually
increases the division in different layers.
2.3. Residual Structure In this paper, referring to the structure of activation
function in PixelCNN [7], two kinds of activation function
Sigmoid and Relu with restriction conditions are adopted, as
shown in equation (3) and (4).
𝑠𝑖𝑔𝑚𝑜𝑖𝑑(𝑥) =1
1 + 𝑒−𝑥 (3)
𝑅𝑒𝑙𝑢(𝑥) = {0, 𝑥 ≤ 0
𝑥, 0 < 𝑥 < 11, 𝑥 ≥ 1
(4)
For the pressure fluctuation prediction problem, the
sigmoid function value is between 0 and 1, which can be
used to predict the pressure fluctuation after standardization.
At the same time, due to the influence of circulating pump
oil and control algorithm, the starting point of pressure in
multiple injections is not always the rated injection pressure.
The relu function with constraints can adjust the amplitude
of the predicted current pressure fluctuation, as shown in
Fig. 3.
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
3
Sigmoid
Dilated Causal
Convolution
Relu function with
constraints
Output
Input
Fig. 3 Visualization of activation layer.
The input in the figure passes through the dilated causal
convolution layer and enters into two activation function
layers respectively for operation. The final output is as
shown in equation (5), where ∗ denotes a convolution
operator and ⨀ denotes an element-wise multiplication
operator.
𝑜𝑢𝑡 = 𝑟𝑒𝑙𝑢(𝑊𝑓 ∗ 𝑥) ⨀𝑠𝑖𝑔𝑚𝑜𝑖𝑑(𝑊𝑔 ∗ 𝑥) (5)
In CNN, if there are too many intermediate layers in the
network, the network performance will be greatly reduced.
This performance decline is not caused by the
disappearance of gradient or the explosion of gradient,
because batch standardization layer has solved these two
problems well. Generally speaking, this kind of complex
network structure reduces the model learning ability, which
is called degradation phenomenon. When the network is
learning, this phenomenon is due to the long feature transfer
path ,so the convolution layer is difficult to learn effective
features. In theory, the deeper network structure should
have better performance than the shallow network. If a part
of the network is transformed into identity mapping, the
network can achieve the same performance as the shallow
network. However, it is difficult to learn identity mapping
by gradient descent method. In order to solve this problem,
residual structure is introduced, as shown in Fig. 4.
Convolution layer
Convolution layer
Relu
Relu
x
F(x)
x
F(x)+x
ResidualShortcut
Fig. 4 Visualization of residual structure.
The basic principle of residual structure is to copy the
calculated output features of a certain layer into two copies,
one of which continues to carry out forward propagation for
subsequent calculation; the other part passes through
several layers of convolution layers and directly adds the
features after several convolution layers. It is difficult to
make the output 𝐹(x)equal to the original output x when
there is no residual error. However, after adding the residual
error, the output 𝐹(x) + x is equal to x, which only needs to
make 𝐹(x) be zero, which is easier for network learning.
2.4. Design of Network Structure In order to smooth the input features, an embedded layer is
used to improve the nonlinearity of the model. In order to
improve the prediction effect of the model, residual
structure and skip connection are adopted. The final
prediction model structure is shown in Fig. 5. In the graph, 1
× 1 is the convolution kernel of size 1, which is mainly used
for dimension transformation. The rectangular box in the
figure is a complete residual module. The activation layer in
the residual module is shown in Fig. 4. Each module
contains its corresponding cavity causal convolution layer.
The original pressure fluctuation data first improves the
nonlinearity through the embedding layer, and then it is
input to the residual module of the first layer. After the
operation of hole convolution and activation layer, one
output is used for skip Connection, the other way is added
together with the residual to input to the next residual
module. Finally, the skip connections of each layer are
added and the final output result is obtained through
sigmoid layer.
Dilated Conv
Activation Layer
1X1
Residual
K Layers
Output
Input
Embedding layer
1X1
1X1 Sigmoid
Predict Outcomes
Skip-connections
Original sample
Fig. 5 The Structure of Algorithm.
3. EXPERIMENTS 3.1. Dataset Generation
In this paper, the original data required by the prediction
model are collected through multiple injections bench tests
of high pressure common rail system. The fuel injection
quantity and injection rate of the injector are measured by
single injection instrument, and the driving current signal of
the injector is measured by current caliper. Since it is
difficult to measure the injection pressure in the injector
needle valve chamber, a high sensitivity pressure sensor is
installed at the high pressure fuel inlet of the injector to
measure the change of injection pressure. Because the law
of injector inlet pressure and injection pressure are almost
the same, there is only a certain phase difference, so the
injector inlet pressure is used to replace the injection
pressure. A total of 750 working conditions with different
rated injection pressure, different injection duration and
different injection interval were collected. The pre main
injection time interval was 0.1ms to 4.0ms under each
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
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working condition, and the step size was 0.1ms. The
sampling frequency is 100kHz.
Since the collected signal contains high-frequency
interference signal, in order to facilitate the construction of
subsequent prediction model, the first-order inertial filtering
is adopted for the inlet pressure, driving current and fuel
injection rate signals to filter out most of the high-frequency
interference signals in the collected signals. The filtering
results are shown in Fig. 6.
Fig. 6 Filtered acquisition signal.
The simulated speed of the bench test is 200 rpm and the
injection interval is 300 ms. Due to the small proportion of
driving current signal and inlet pressure signal in the whole
cycle, a large number of meaningless data will be mixed
with the whole sequence for subsequent processing, which
makes the sample sparsity too high. In order to reduce the
sparsity of the original data, the data about 12ms from the
beginning of the pre injection duration to the main injection
duration and the gradual attenuation of the pressure
fluctuation are intercepted. Since the acquisition frequency
of the bench test is 100kHz, there are about 1200 sampling
points in a cycle.
The causes of multiple injections pressure fluctuation in
common rail system are complex. Through the analysis of
formation mechanism and dynamic characteristics of
multiple injections pressure fluctuation in high-pressure
common rail system, it can be seen that rated injection
pressure and pre main injection time interval will have a
greater impact on pressure fluctuation. In addition,
according to the mathematical model and related research of
high pressure common rail system, the structure of each
component in the system will also have different degrees of
influence on the pressure fluctuation characteristics. The
original characteristics which have great influence on
pressure fluctuation collected in this study are shown in
Table 1.
The structural characteristics of each component in the
system are unstructured data. Unstructured data refers to the
data that is not conducive to the model to read and process
directly. Since the structure and environmental pressure of
various components in the system are constant, such
features will not be selected as basic features.
After selecting the basic features, in order to improve the
information contained in the data, it is necessary to
manually construct new features from the basic features.
Using the potential structure and physical meaning of the
features, new features are created by combining and
transforming the basic features. The new features are shown
in Table 2.
The propagation time of pre-injection pressure fluctuation
in the table has a clear physical meaning with the overall
duration of pre-injection. The propagation time of
pre-injection pressure fluctuation represents the
propagation time of pressure fluctuation caused by
pre-injection before the start of main injection process; the
overall duration of pre-injection represents the total time
from the signal generation of driving current to the complete
end of pre-injection. In physical sense, the rated injection
pressure interval ratio and pre main injection time ratio are
not as obvious as the former two, but through the fusion of
2020 2030 2040 2050 2060 2070120
128
136
144
152
160 入口压力 电流
时间 (ms)
入口压力
(M
Pa)
-10
0
10
20
30
40
50
60
70
80
电流
(A
)
Table 1 Basic characteristic variables of pressure fluctuation.
Characteristic Variable Variable Type Data Type Unit Driving Current Time Series FLOAT16 𝐴
Injector Inlet Pressure Time Series FLOAT16 𝑀𝑃𝑎
Injection Rate Time Series FLOAT16 𝑚𝑚3𝑚𝑠−1
Rated Injection Pressure Structured Data FLOAT16 𝑀𝑃𝑎
Pre-main injection interval Structured Data FLOAT16 𝑚𝑠
Starting Point of Pre-Injection Driving Current Structured Data FLOAT16 𝜇𝑠
Starting Point of Pre-Injection Rate Structured Data FLOAT16 𝜇𝑠
Finish Point of Pre-Injection Driving Current Structured Data FLOAT16 𝜇𝑠
Finish Point of Pre-Injection Rate Structured Data FLOAT16 𝜇𝑠
Duration of Pre-Injection and Main Injection Structured Data INT 𝑚𝑠
Environmental Pressure Structured Data INT 𝑀𝑃𝑎
Structural Characteristics of Components in the System Unstructured Data NONE NONE
Table 2 Characteristic variables constructed.
Characteristic Variable Construction Method Data Type
Propagation Time of Pre-Injection Pressure
Fluctuation
Starting Point of Pre-Injection Driving Current - Pre-main
injection interval INT
Duration of Pre-Injection Finish Point of Pre-Injection Rate - Starting Point of
Pre-Injection Driving Current INT
Rated Injection Pressure Interval Ratio Rated Injection Pressure / Pre-main injection interval FLOAT16
Pre-Main Injection Time Ratio Duration of Pre-Injection / Duration of Main Injection FLOAT16
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The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
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different basic characteristics, it is conducive to enhance the
understanding of the model for data, accelerate the
convergence speed of the prediction model and improve the
prediction accuracy of the model.
In order to predict the future development of the
fluctuation through the pressure fluctuation information of
tenure period, it is necessary to construct samples for model
training or learning from the data level. In this paper, the
sliding window method is used to divide the original
learning samples. The schematic diagram of sliding window
is shown in Fig. 7.
Fig. 7 Sliding window sampling.
3.2. Data Standardization In order to prevent the model from producing partial
distribution among different features, it is necessary to
standardize the original data. In this paper, there are two
methods of Standardization: deviation standardization and
Z-Score standardization. Deviation standardization is a method of linear
transformation of the original data, so that the original data
falls into [0,1]. The conversion function is shown in formula
(6)
𝑥′ =𝑥 − min (𝑥)
max(𝑥) − min (𝑥) (6)
where 𝑥 is the original feature, min is the minimum value of
the data, and max is the maximum value of the data. The
deviation standardization depends on the maximum and
minimum value of the data. If the data is increased or
decreased or updated, it needs to be standardized again.
When the data is stable, the deviation standardization is
better.
Z-Score standardization also uses linear transformation to
put the original data in [0,1]. The conversion function is
shown in equation (7)
𝑥′ =𝑥 − μ
σ (7)
where μ is the expectation of the original data, σ is the
standard deviation of the original data. The Z-Score
standardization is applicable to the case that the maximum
and minimum value of the original data is unknown or there
are outliers with extreme values.
The two kinds of standardization will not change the
distribution of the original data, which is conducive to the
data to retain the original information.
3.3. Experimental Result And Analysis According to the pressure fluctuation model to predict the
demand, the model needs to predict the injector inlet
pressure at the remaining sampling points of the cycle
according to the pressure fluctuation information at the
sampling points during the pre-injection duration.
According to the causal convolution principle, the output
result 𝑥𝑡 of the model at a certain position is related to the
sequence information (𝑥𝑡−𝑛 , … , 𝑥𝑡−1) from t-n to t-1. If the
model can predict the subsequent fluctuation according to
the pressure fluctuation information in the pre-injection, it is
necessary to design the receptive field of the model.
According to the receptive field calculation formula (2), the
network layer number is calculated. Considering the
complexity of the model and the ability of computing
platform hormone, the structural parameters of causal
convolution of the prediction model are shown in Table 3.
As shown in the table, the CNN structure constructed in
this paper shared 10 layers of hole causal convolution, and
the convolution void rate of each layer gradually increased
from 1 to 52. The increasing void rate increased the growth
rate of receptive field, and gradually expanded the receptive
field to 200, so as to complete the information capture
during the pre spray duration.
In order to verify the effectiveness of the prediction model,
this paper carries out comparative experiments between
different model structures and different training strategies,
and analyzes the results. The relevant parameters of the
model are shown in Table 4. The experimental results of
each model are shown in Table 5. In order to verify the
influence of different receptive fields on the prediction
accuracy, the residual block of the original model needs to
be deleted. The receptive field of the original model is 200,
corresponding to 10 residual modules and the empty causal
convolution layer in each residual module. In order to
modify the receptive field to 100 and 50, the corresponding
residual block should be deleted according to table 3.
Table 3 The structure of causal convolution.
Layers of Dilated Causal
Convolution The Size of
Convolution Kernel Division The size of Equivalent Convolution Kernel Receptive Field
1 layer 2 1 2 2
2 layer 2 2 3 4
3 layer 2 4 5 8
4 layer 2 8 9 16
5 layer 2 9 10 25
6 layer 2 25 26 50
7 layer 2 25 26 75
8 layer 2 25 26 100
9 layer 2 50 51 150
10 layer 2 51 52 200
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The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
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It can be seen from the results in the table that with the
increase of receptive field, the accuracy of the model
gradually increases. Because the receptive field is increased
by adding convolution layer, the training time is also
gradually increased. Due to the increase of receptive field,
the network can obtain more time series information and
higher prediction accuracy in pressure fluctuation
prediction, which conforms to the principle of network
design. The prediction effect of different receptive fields is
shown in Fig. 8. Fig. 8(a) shows the comparison of model effect between
receptive field 50 and receptive field 200. The model with
receptive field 50 in the figure has better effect in the
injection interval and main injection duration, but the
prediction of pressure fluctuation in the later stage is
inaccurate. Fig. 8(b) shows the effect comparison between
receptive field 100 model and receptive field 200 model. As
shown in the figure, the model of receptive field 100 can
basically predict the pressure fluctuation, and the effect is
good, but there are some jitters in some parts.
In order to further verify the effectiveness of the prediction
model designed in this chapter, the results of various
working conditions such as different target rated injection
pressure and different injection interval were visualized.
For the same rated injection pressure and different pre main
injection time interval, the experimental results are shown
in Fig. 9. The red solid line is the predicted value of
pressure fluctuation, and the black solid line is the real value
of pressure fluctuation. The injection time interval in the
figure changes from 0.5ms to 4.0ms. From the prediction
effect, it can be seen that the predicted pressure fluctuation
is in good agreement with the real fluctuation curve in the
injection time interval and the main injection duration, and
the prediction curve is relatively smooth, and the prediction
accuracy for the serrated pressure fluctuation in the main
injection duration is slightly lower. In order to further verify
the generalization ability of the model, this experiment also
visualized the prediction effect of the model under the same
injection time interval and different rated injection pressure,
as shown in Fig. 10. The injection time interval in each
diagram is 4ms, and the rated injection pressure is 80MPa,
100MPa and 140MPa respectively. It can be seen from the
prediction results that the prediction results of the model
under different rated injection pressures meet the
2 4 6 8 10 1285
90
95
100
预测值
/MP
a
时间/ms
目标值 感受野(50) 感受野(200)
2 4 6 8 10 1285
90
95
100
预测值
/MP
a
时间/ms
目标值 感受野(100) 感受野(200)
(a)Receptive field = 50 and 200 (b)Receptive field = 100 and 200
Fig. 8 Prediction effect of different receptive field models.
Table 4 The structure of causal convolution.
Parameter Name Parameter Value
The Number of Training Set Samples 48000
The Number of Test Set Samples 12000
The Length of The Input Sequence 200
The Length of Prediction Sequence 1000
Learning Rate 0.0001
Batch Size 16
The Number of Characteristic Variables 6
Optimizer Adam
Loss Function Mean Square Error (MSE)
Evaluating Indicator MSE、Mean Absolute Percentage Error (MAPE)
Table 5 The structure of causal convolution.
Model Step Training Time (s)
Overall Training Time (min)
The Number of Iterations
MSE of Training Set
MSE of Test Set
MAPE of Test Set
WaveNet (Receptive
field =50) 0.032 80.12 150000 4.5e-4 6.5e-4 1.365
WaveNet (Receptive
field =100) 0.051 127.5 150000 1.2e-4 2.7e-4 0.964
WaveNet (Receptive
field =200) 0.087 217.5 150000 6.7e-5 9.7e-5 0.822
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The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
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requirements of prediction, and the accuracy is high, which
proves that the model has strong generalization ability.
2 4 6 8 10 1270
75
80
85
90
95
100
喷油间隔
预测值 真实值 喷油速率
时间/ms
预测值
/MP
a
0
10
20
30
40
50
60
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/mm
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(a)injection interval is 0.5ms
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(d)injection interval is 2.0ms
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(e)injection interval is 2.5ms
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(f)injection interval is 3.0ms
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/mm
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(h)injection interval is 4.0ms
Fig. 9 The prediction results of different injection intervals while
the target injection pressure is 100MPa,.
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020
A Pressure Fluctuation Prediction Algorithm for High Pressure Common Rail System Based on CNN
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)
CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
8
2 4 6 8 10 1255
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/MP
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/mm
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(a) Target injection pressure is 80MPa
2 4 6 8 10 12
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/mm
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(b) Target injection pressure is 100MPa
2 4 6 8 10 12
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预测值 真实值 喷油速率
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/mm
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(c) Target injection pressure is 140MPa
Fig. 10 The prediction results of the model under different target
injection pressures while the injection interval is 0.4ms.
The average prediction accuracy of the model is about
99.1%. The prediction accuracy of the pressure fluctuation
from the injection interval to the starting point of the main
injection is higher, and the prediction accuracy of the
pressure fluctuation and its attenuation process after the
main injection duration decreases. On the one hand, the
decline of accuracy is caused by the accumulation of errors
in the process of increasing the prediction length of the
model; on the other hand, the pressure fluctuation after the
main injection is greatly affected by random factors which
is random and unpredictable.
CONCLUSION In order to meet the emission requirements, the
high-pressure common rail system of diesel engine has
become more and more strict for the precise control of the
circulating oil quantity. Because the pressure fluctuation
caused by the pre-injection of high pressure common rail
system will affect the pressure of main injection, which will
affect the injection fuel quantity, reduce the consistency of
circulating oil quantity, and then affect the fuel injection
quantity. Therefore, accurate prediction of pressure
fluctuation is the premise of controlling the circulating oil
quantity. In this paper, the multiple injections’ pressure
fluctuation of high pressure common rail system is
predicted by deep learning method.
Based on the structure of WaveNet, the pressure
fluctuation prediction algorithm of high pressure common
rail system is proposed and a lot of comparative
experiments are carried out. The results of the experiment
show that the average accuracy of the prediction algorithm
is about 99.1%, and meets the demand. The accuracy of the
prediction the pressure fluctuation from the injection
interval to the starting point of the main injection is higher,
and the accuracy of the prediction of the pressure
fluctuation and its attenuation process slightly decreases
after the main injection duration. The size of the sequence
length has little effect on the accuracy of prediction. The
results show that the larger the receptive field is, the higher
the accuracy of the model is. However, since the duration of
pre-injection in this rail pressure experiment is about 200,
the prediction effect is the best when the receptive field is
200.
REFERENCES: [1] Liyun F, Yun B, Si J, Yang L, Xiuzhen M. Fuel Injection Quantity
Fluctuation in Multiple Injections of High-Pressure Common-Rail Fuel Injection System. Journal of Harbin Engineering University, 2016, 37(08): 1063-9.
[2] Mohebbi M, Aziz A A, Hamidi A, et al. Modeling of Pressure Line Behavior of A Common Rail Diesel Engine Due to Injection and Fuel Variation. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2017, 39(3): 661-9.
[3] Ubertini S. Injection Pressure Fluctuations Model Applied to A Multidimensional Code for Diesel Engines Simulation. Journal of Engineering for Gas Turbines and Power, 2006, 128(694-701).
[4] Catalano L, Tondolo V, Dadone A. Dynamic Rise of Pressure in the Common-Rail Fuel Injection System. 2002.
[5] Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press, 2016.
[6] Oord A V D , Dieleman S , Zen H , et al. WaveNet: A Generative Model for Raw Audio. 2016, arXiv:1609.03499.
[7] Van Den, Oord A, Kalchbrenner N, Vinyals O, Espeholt L, Graves A, Kavukcuoglu K. Conditional Image Generation with PixelCNN Decoders. 2016, arXiv:1606.05328.
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020)CITIC Jingling Hotel Beijing, Beijing, China, Oct.31-Nov.3, 2020
The 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020) Beijing, China, Oct.31-Nov.3, 2020