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proposed. In (Sugumaran et al., 2007), the authors
use the decision tree to identify the best features in
classification task, they use a proximal support
vector machine characterized by its capability to
classify efficiently the faults in Roller Bearning
system.
In this paper, new methodology dealing with
small leaks detection problem in diesel air path is
developed. To achieve this goal, a new scheme
based on neural network technique is proposed. The
nominal mode (without leak) and leakage mode
corresponding to several diameters of leak were
trained using a Levenberg-Marquardt algorithm.
Before using the acquired data, a feature selection
task is proposed in order to reduce the complexity of
the problem. The main challenge of the proposed
approach is the use of a selected sensors leading to a
reduced cost. The data of two considered modes are
generated using xMOD platform which will be
described later.
The paper is organized in this way. First, the
considered problem is presented in section 2.
Section 3 describes the proposed approach in details
where a brief description of neural networks used,
which based on steepest-descent and Gauss-Newton
method, is given and main detection scheme is
illustrated. After a brief description of xMOD tool
used in engine data collecting, the section 4 gives
some obtained results using our approach. Then,
these results are discussed and commented in order
to illustrate the effectiveness of leakage detection.
2 PROBLEM STATEMENT
For several years, the anti-pollution standards are
dramatically increased and the constraints in
automotive industry become very complex. The
main objective of these standards is to reduce the
emissions level of cars. In the case of diesel engines,
there are several pollutants: carbon monoxide,
unburned hydrocarbons, nitrogen oxides (NOx) and
diesel particulates mater. Usually, the emissions
level proportionally increases with the appearance of
faults in diesel engines, more precisely in diesel air
path. These faults can be due to sensor failures,
actuator failures or system degradation. In this
paper, the last failures class is considered. More
precisely, the leakage detection in diesel air path is
studied. This failure can cause multiple non-desired
system behaviour. In addition to the high emissions
level, this failure causes multiple non-desired effects
such as:
Operating points changing of the air path
subsystems,
Incomplete combustion in cylinders,
Appearance of smoke and the reduction of
performances.
Often, this type of failure can be confused with
the two other types of faults, i.e. sensors or
actuators; consequently, it is very important to
distinguish this fault from others.
Additionally to the main objective of this paper,
the feature selection problem is considered. We all
know that today’s vehicles are characterized by an
increased complexity justified by the important
number of embedded sensors which grow
significantly. Consequently, the uses of selected
subset of sensors data which are correlate the
considered problem is widely desired in such
applications.
In this work, our main objective is to detect air
leaks in diesel air path regardless of their diameters.
Before performing leaks detection, we make a
feature selection in order to reduce the data
complexity.
It is important to specify that, for this
application, small leaks are hidden and are very
difficult to detect because of phenomenon of non-
solicitation system.
3 PROPOSED APPROACH
Nowadays, the neural network is an essential tool
used in many research activities for industrial
complex systems. An advantage of using neural
network to detect faults of systems is that it can get
the knowledge denoted by data. Over and above
remembering ability of learned information, a neural
network has both, ability to generalize an obtained
model and apply the associative property into the
available memory. The error tolerance,
characterizing the neural network, effectively treats
the errors of the model. Additionally, it can perform
a nonlinear mapping and also learn dynamic
behaviors in order to generalize the obtained models.
Generally, the collected data for detection
process are noisy, but, the error tolerance ability of
neural network makes the detection scheme be able
to differentiate the pattern from noise. So, the last
property is a huge advantage in fault detection and
isolation problem. Additionally, the similar patterns
are separated using a generalization property
characterizing a neural network.
The leak detection in intake system is very
difficult to achieve especially when the operating
point corresponds to low load-torque couple. In
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