SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM
USING A CLASSIFICATION APPROACH
Oriane Le Pocher, Eric Duviella
Univ. Lille Nord de France, F-59000 Lille, France
EMDouai, IA, F-59500 Douai, France
Karine Chuquet
VNF - Service de la navigation du Nord Pas-de-Calais, 37 rue du Plat, 59034 Lille Cedex, France
Keywords:
Supervision, Fault detection, Classification algorithm, Large scale system, Hydraulic system.
Abstract:
This paper focuses on the sensor fault detection of a hydraulic channel used for navigation. This system has the
particularities to have large scale dimension, without slope, with several inputs and ouputs, and thus difficult
to be modelled according to classical modelling methods. For recent years, it was equipped with level sensors
in order to have better knowwledge of its behavior, to detect its state online and thus improve its management.
However, level sensors are subjected to measurement or transmission errors, setting errors, and quick or slow
drifts. In order to detect these sensor errors, a classification approach is proposed. It appears adapted to the
fault detection of large scale hydraulic systems without model. The classification approach is used on data
measured from 2006 to 2009. The first results and analysis show that the classification method is effective for
addressing the problem of sensor fault detection.
1 INTRODUCTION
Hydrographical networks are large scale systems
characterized by nonlineardynamics and varying time
delays. They are used for several human activities,
especially navigation and transport. In the northern
Europe, navigation channels assure the transport of
goods with the objective, within a few years, of ac-
comodating large broad gauge boats. The control of
the water level in navigation channels becomes cru-
cial. In order to achieve this objective, sensor net-
works have been implemented. These sensors allow
the measurement of water levels or water discharges,
and the implementation of levelcontrol algorithms for
a local water management. At a larger scale, the level
and discharge measurements are essential to provide
an efficient water management of the navigation chan-
nel networks, by mainly characterizing their state on-
line. However, sensor networks are impacted by mea-
sure errors, transmission faults, or drifts of operation.
So, in order to improve the management of navigation
channels, sensor fault detection techniques have to be
employed.
Fault Detection and Isolation (FDI) techniques
are largely proposed in the litterature and employed
by following a systematic approach. The first step
consists in characterizing the operating modes of the
system to be supervised. Several model-based ap-
proaches were proposed (Frank et al., 2000), based
on parameters identification technique (Weihua et al.,
2003), parity equations method (Gertler, 1998), diag-
nosis observers (Akhenak et al., 2004), or Kalman fil-
ters (Xie et al., 1994). Even if these FDI techniques
have proven to be as powerful and effective, they re-
quire an accurate model of the system by minimizing
the uncertainties and the process noise. Very recently,
fault detection methods based on residual generation,
extended Kalman filter and finite memory observer
are proposed in (Bedjaoui and Weyer, 2010), in order
to detect and localize leak in an irrigation network.
This detection method is based on physical hydraulic
system model, in particular on the Saint-Venant par-
tial differential equations (Chow et al., 1998). How-
ever, due to their physical characteristics, i.e. large
dimensions, no slope, etc., navigation channels can
not always be modelled using physical laws without
requiring numeric models. In this way, traditionnal
FDI techniques cannot reach the fault detection aims.
When the physical modelling of the system is
not realizable, pattern recognition techniques consti-
382
Le Pocher O., Duviella E. and Chuquet K..
SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH.
DOI: 10.5220/0003571603820387
In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2011), pages 382-387
ISBN: 978-989-8425-74-4
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
tute an interesting alternative approach for fault de-
tection problem. They consist in extracting informa-
tion on the system state by using the signals collected
from sensors (Hartert et al., 2010). The operating
modes of the system are represented by classes which
are built according to dynamic classification algo-
rithms, as the CDL algorithm (Cluster Detection and
Labeling), algorithms based on adaptive resonance
theory (ART) networks (Su and Liu, 2005), (Eltoft
and de Figueiredo, 1998), FMC algorithm (Fuzzy
Min-max Clustering) (Mouchaweh et al., 2002) or
AUDyC (Auto-Adaptive Dynamical Clustering) al-
gorithm (Lecoeuche et al., 2004). In (Traore et al.,
2009), the AUDyC algorithm is employed to super-
vise a thermo-regulator system subjected to slow and
quick drifts in its dynamics.
This paper focuses on sensor fault detection of the
Cuinchy-Fontinettes navigation channel. This sys-
tem, located in the north of France, is characterized
by large dimensions and no slope. Thus, it is not pos-
sible to be modelled according to physical approach.
Thus, a classification approach is proposed to address
the fault detection problem. In section II, the real hy-
draulic system and the problem of sensor fault detec-
tion are presented. The classification approach based
on AUDyC algorithm is detailed in section III. This
fault detection technique allows the determination of
indicators characterizingthe real-time drift of sensors.
Finally, in section V, the proposed approach is applied
on real data measured from 2006 to 2009, and its per-
formance for the detection of sensor faults are high-
lighted.
2 PROBLEM STATEMENT
The channel studied is the reach Cuinchy-Fontinettes
which is located in the North of France between the
lock of Cuinchy at the East of the town Bethune and,
at the Southwest of the town Saint-Omer, the lock of
Fontinettes (see Figure 1). With 42.3 km long and
51.8 m large, this reach is part of the broad gauge
river network of North of France. It is characterized
by no significant slope. It can handle boats until 185
m long and 11.4 m large. This channel is entirely arti-
ficial. The first part of the channel, i.e. 28.7 km from
Cuinchy to Aire sur la Lys, is called ”canal d’Aire”
and has been built in 1820. The second part of the
channel, i.e. 13.6 km from Aire sur la Lys to Saint
Omer, is called ”canal de Neuffoss´e” and has been
built in the eleventh century.
The Cuinchy-Fontinettes is managed by VNF
(Voies Navigables de France). The role of VNF is
to maintain the level of the channel at NNL = 19.52
m to allow the navigation (NNL = Normal Naviga-
tion Level). The main issue for the management of
this reach is to counterbalance the navigation flow
of 3 m
3
/s at Fontinettes. A part of the water comes
from the navigation flow at Cuinchy which represents
0.6 m
3
/s. The difference between the two navigation
flows comes from the size of the lock at Fontinettes
which is 13 m high whereas the lock at Cuinchy is
only 2 m high. The counterpart of water comes from
different hydrants and discharges. A high number of
rivers are inverted siphon and pass under the reach
but three of them feed directly the reach. Another
solution to feed the reach is the Cuinchy gate. This
gate allows a controlled feeding of the reach with the
water of the Deˆule river. In the same way, the gate
called ”Porte de Garde” at Aire sur la Lys allows con-
trolled exchanges between the Lys river and the reach.
Finally a high number of anthropogenic discharges
(more than 320) feed the reach in an unknown way.
Fontinettes Lock
Gate ‘Porte de Garde’
Cuinchy lock
Fort Gassion lock
Aire basin
Fort Gassion gate
Fontinettes level sensor
Aire level sensor
Cuinchy level sensor
Figure 1: Scheme of the navigation canal Cuinchy-
Fontinettes.
For recent years the reach has been equipped with
level sensors in order to better know its behaviour.
The level sensors used for this study are located
downstream from the lock of Cuinchy, upstream from
the lock of Fontinettes and in the Aire basin. The
Cuinchy and Aire sensors are composed of an ultra-
sonic transducer linked with a level transformer. The
Fontinettes sensor is a Probe with a transducer inte-
grated. The technology used is based on ultrasonic
sound and allows the processing of echoes. The data
processing and transmission are realized by telepro-
cessing equipments. The three sensors deliver the
mean of the levels measured every quarter hour. The
waves due to navigation or Fontinettes lock opera-
tions are averaged.
Sensors are subjected to the weather and the en-
vironment and as every electronic device can break
down or be impacted over time. Several types of er-
rors can occur. A bad setting of the sensors can lead to
systematic errors. Aberrant data are caused by local
and temporal errors. Blockade of data can be due to
SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH
383
a transmission fault for example. Level sensor can be
subjected to slow temporal drifts. In order to use re-
liable measured data, it is essential to propose a fault
detection technique.
The main problem for the fault detection tech-
nique proposal is the major difficulty of modeling
the Cuinchy-Fontinettes channel without numeric ap-
proach. A fault detection technique by a pattern
recognition approach is proposed in the next section
in order to be freed from a model of the channel.
3 FAULT DETECTION BY A
PATTERN RECOGNITION
APPROACH
The fault detection method proposed in this paper is
based on a classification technique. This classifica-
tion technique consists in characterizing an operating
mode of the dynamic system by a Gaussian model
which constitutes a class. A class is determined ac-
cording to pertinent selected data which present same
similarities. According to these selected data, a Rep-
resentation Space can be built, and the class can be
represented in this space. Thus, the class of the nor-
mal operating mode, denoted C
n
, can be determined
(see Figure 2). A new class is create when a sufficient
number of points is present in an area of the Represen-
tation Space. The new class, which is updated or cre-
ated, is denoted evolutionary class C
e
. It corresponds
to a new operating mode.
When a measurement or transmission error oc-
curs, a new point appears in the Representation Space
far from the normal class C
n
(see Figure 2.a). This
point has to be detected and rejected if it is isolated.
When the level sensor is subjected to slow drifts, the
class updates online (see Figure 2.b). The character-
istics of the normal class evolve during time. Finally,
catalectic failures lead to a jump in the representation
space (see Figure 2.c).
x
1
x
1
x
1
x
2
x
2
x
2
(a)
(b)
(c)
C
n
C
n
C
n
C
e
C
e
Figure 2: (a) Measurement or transmission errors, (b) slow
drifts, (c) jump characterising catalectic failure, in a two
dimensions representation space.
The classification technique which is proposed to
monitor slow drifts and jumps, is based on the AU-
DyC algorithm. AUDyC is an evolutionary data clas-
sification algorithm whose role is to model, in a con-
tinuous way, the operating modes of dynamical sys-
tems. The technique is inspired from the mixed Gaus-
sian model (Lecoeuche et al., 2004). The Gaussian
classes are represented by prototypes P
j
characterized
by a center and a matrix of covariance. The proto-
types characteristics are adapted to each new obser-
vation X = (x
1
, x
2
, ... x
n
), with n the number of per-
tinent data, by using rules of recursive update on a
sliding window of size N
fen
, and by considering the
totality of the prototype P
j
, noted Card (P
j
) on previ-
ous instant k 1, according to the algorithm described
below. A new observation X is rejected if is too far
from the current class. In other case, this observation
is assigned to one of the N existing classes according
to an adaptation procedure of the prototypes.
If Card(P
j
)=nb < N
fen
: add information
M
P
j
(k) = M
P
j
(k 1) +
1
nb+ 1
(X
k
M
P
j
(k 1)),
P
j
(k) =
nb 1
nb
P
j
(k 1)+
1
nb+ 1
(X
k
M
P
j
(k 1))
(X
k
M
P
j
(k 1)).
(1)
If nb N
fen
: add or retreive information
M
P
j
(k) = M
P
j
(k 1) +
1
N
fen
(δX
+
δX
),
P
j (k) =
P
j (k 1)+
X
1
N
fen
1
N
fen
(N
fen
1)
1
N
fen
(N
fen
1)
(N
fen
+1)
N
fen
(N
fen
1)
X
,
(2)
where
δX
+
= X
new
M
P
j
(k 1),
δX
= X
old
M
P
j
(k 1),
X = [δX
+
δX
],
(3)
with M
P
j
(k) and
P
j
(k) respectively the center and
the covariance matrix of the prototype P
j
at instant k,
N
fen
the width of the slipping window, X
new
and X
old
new and old observation vectors, respectively.
This fault detection method based on AUDyC al-
gorithm allows rejecting measurement or transmis-
sion errors, following slow drifts and catalectic fail-
ures. In order to detect drifts, fault indicators have
to be calculated. A first indicator corresponds to the
mean of the measured levels on the sliding window
N
fen
. It allows the visualisation of quick and slows
drifts. A second indicator consists in computing the
ICINCO 2011 - 8th International Conference on Informatics in Control, Automation and Robotics
384
distance between the evolutive class C
e
and the nor-
mal class C
n
. The normal class is taken as reference.
A fault occurence can lead to the drift of the evolu-
tive class C
e
from the normal class C
n
. Increasing of
this distance reveals, in some cases, the presence of
faults. Amongst the several existing metrics, the Eu-
clidian distance, denoted d(M
e
,M
n
), is considered:
d(M
e
,M
n
) =
q
(M
e
M
n
)(M
e
M
n
)
, (4)
with M
e
is the center of the class C
e
and M
n
the center
of the normal classC
n
. The center of the normal class
M
n
is fixed.
Moreover, by considering, for causal systems, that
the outputs depend on the variation of the inputs, cor-
relation indicators between the center of the evolutive
class according to each direction of the Representa-
tion Space, denoted K
i, j
for data x
i
and x
j
, can reveal
a fault occurence. That leads to compute correlation
coefficients between the mean of each measured level
on a sliding window of size N
fen
. In a second step, er-
ror indicators are computed between the center of the
evolutive class amongst each direction of the Repre-
sentation Space also. The error indicators are denoted
ε
i, j
for data x
i
and x
j
. Finally, the quadratic error in-
dicators ε
2
i, j
are computed according to ε
i, j
.
Quadratic error indicators ε
2
i, j
are used in order to
detect setting errors. Error indicators ε
i, j
are used in
order to detect slow drifts and to determine which sen-
sor is faulty. Finally, correlation indicators K
i, j
al-
low the detection of quick drifts and catalectic fail-
ures. The redondancy of the indicators and a cross-
comparison lead to determine which of the sensors is
faulty. The fault detection approach is applied in the
case of the Cuinchy-Fontinettes Channel on data from
2006 to 2009. Results and analysis are presented in
the next section.
4 FAULT DETECTION IN THE
CUINCHY-FONTINETTES
CHANNEL
Measured data on the Cuinchy-Fontinettes Channel
correspond to x
a
, x
c
and x
f
, for Aire in the middle
of the channel, for Cuinchy at the upstream, and for
Fontinettes at the downstream, respectively. These
data are measured with a sample time equal to 15
minutes from 2006 to 2009. It represents more than
135000 × 3 values. These data were not recorded at
the same time. Indeed, there are discrepancies of few
minutes between measurements. Then, the first step
is to resynchronize all the data.
The second step consisted in building the Repre-
sentation Space with the three measured data x
a
, x
c
and x
f
, and to represent the normal class C
n
(see Fig-
ure 3). The normal class is built with accurate mea-
sured data from April to June 2006 according to a slid-
ing window N
fen
equal to 2500 values. The center of
the class C
n
is around zero (relative levels according
to the NNL), i.e. [0.034 0.058 0.059], and its
covariance matrix is equal to:
Σ
n
=
0.0060 0.0052 0.0055
0.0052 0.0055 0.0052
0.0055 0.0052 0.0065
. (5)
Figure 3: Normal class C
n
in the Representation Space.
Although the total measured data was used, only
years 2006 and 2009 were shown to highlight the per-
formance of the proposed approach. Figure 4 shows
the measured levels, i.e. x
c
, x
a
and x
f
, and the de-
tection and isolation of wrong measured data during
the year 2009. The wrong data are depicted by black
cross in Figure 4. The classification approach allows
to reject these points automatically.
Figure 4: Data x
c
(continuous line), x
a
(dotted line) and x
f
(dashed-dotted line) measured on 2009, and isolate wrong
data (black cross).
Figure 5.a shows the measured levels in Cuinchy
(blue continuous line), in Aire (red dashed line)
and in Fontinettes (green dashed-dotted line) during
2009. The distance d(M
e
,M
n
) is depicted in Figure
SENSOR FAULT DETECTION IN A REAL HYDRAULIC SYSTEM USING A CLASSIFICATION APPROACH
385
5.b. The correlation indicators K
c,a
, K
c, f
and K
a, f
,
the quadratic error indicators ε
2
c,a
, ε
2
c, f
and ε
2
a, f
, and
the error indicators ε
c,a
, ε
c, f
and ε
a, f
, are computed
between the mean of measured levels in Cuinchy
and Aire, in Cuinchy and Fontinettes, in Aire and
Fontinettes, respectively. Indicators between Cuinchy
and Aire are depicted in blue continuous line, those
between Cuinchy and Fontinettes in red dashed line,
and those between Aire and Fontinettes in green
dashed-dotted line, in Figure 5.c, 5.d and 5.e, respec-
tively. A threshold defined equal to 0.7 is considered
in order to detect fault when one of the correlation
indicators is under this threshold (see Figure 5.c).
−0.1
0
0.1
0.2
(a)
Level mean [m]
−0.3
−0.2
−0.1
0
(b)
d
Eu
0
0,5
1
(c)
K
i,j
0
0.01
0.02
(d)
ε
2
i,j
0 1 2 3 4 5 6 7 8 9 10
x 10
4
−0.05
0
0.05
0.1
0.15
(e)
ε
i,j
Time [step]
Figure 5: (a) Measured levels x
c
(continuous line), x
a
(dot-
ted line) and x
f
(dashed-dotted line), (b) Euclidienne dis-
tance d
Eu
(M
e
,M
n
), (c) correlation indicators K
c,a
(continu-
ous line), K
c, f
(dashed line) and K
a, f
(dashed-dotted line),
(d) quadratic error indicators ε
2
c,a
(continuous line), ε
2
c, f
(dashed line) and ε
2
a, f
(dashed-dotted line), (e) error in-
dicators ε
c,a
(continuous line), ε
c, f
(dashed line) and ε
a, f
(dashed-dotted line), measured on 2009.
During 2009, by considering the distance
d(M
e
,M
n
) (see Figure 5.b), only one period around
the 90000
th
step, is relevant to significant drift of the
class C
e
. However, around the 90000
th
sample, oth-
ers indicators are close to their objective values. This
means that there is no fault. In this period, the dis-
tance d(M
e
,M
n
) is increasing because there is a mod-
ification of the operating mode, which can be the con-
sequence of flood (see measured levels in Figure 5.a).
Figure 5.c shows three periods where correlation in-
dicators are under the fixed threashold, i.e. around
20000
th
, 66000
th
and 88000
th
samples. For the two
first periods, there are no significant errors ε
2
and ε
(see Figure 5.d, 5.e). During the third period around
the 88000
th
sample, there are significant errors ε
2
c, f
,
ε
2
a, f
, ε
c, f
and ε
a, f
. Errors ε
2
c,a
,and ε
c,a
are close to
their objectives. This is relevant of slow drift on the
level sensor in Fontinettes.
Figure 6 shows the measured levels, i.e. x
c
, x
a
and x
f
, during the year 2006. The detected wrong
measured data appear during the period around the
55000
th
sample (see black cross).
0 1 2 3 4 5 6 7 8 9 10
x 10
4
−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
Time [steps]
Level [m]
Figure 6: Data x
c
(continuous line), x
a
(dotted line) and x
f
(dashed-dotted line) measured on 2006, and isolate wrong
data (black cross).
The same indicators are determined for year 2006
and depicted in Figure 7, and the same conclusions
can be obtained if the distance d(M
e
,M
n
) and corre-
lation indicators are taken into account. The most in-
teresting point to show is the detection of slow drift
of the Cuinchy level sensor during all the year. From
the beginning of year 2006 to the 26000
th
sample,
quadratic errors ε
2
c,a
and ε
2
c, f
are constant and around
0.0025 and ε
2
a, f
is close to zero (see Figure 7.d). It
means that there is a setting error on the level sensor
in Cuinchy. The setting error is evaluated from 0.05 m
according to the errors ε
c,a
and ε
c, f
(see Figure 7.e).
Then from the 26000
th
sample to the 65000
th
sam-
ple, all the errors are close to zero. It is possible to
assume that the Cuinchy level sensor is correctly set.
Finally, from the 65000
th
sample, quadratic errors ε
2
c,a
and ε
2
c, f
are increasing. It is relevant of slow drift of
the Cuinchy level sensor. Errors ε
c,a
and ε
c, f
are de-
creasing to reach 0.1 m.
The fault detection method proposed in this article
allows the detection of error setting, slow and quick
drifts. It can be implemented online in order to detect
these types of faults in real-time.
5 CONCLUSIONS
The sensor fault detection of real large scale systems
without model is an interesting research problem. The
well-known classical FDI techniques cannot be ap-
plied due to the difficulty of modelling. Thus, a fault
detection approach without model is proposed in or-
ICINCO 2011 - 8th International Conference on Informatics in Control, Automation and Robotics
386
−0.1
0
0.1
(a)
Level mean [m]
−0.06
−0.03
0
0.03
(b)
d
Eu
0
0,5
1
(c)
K
i,j
0
0.005
0.01
(d)
ε
2
i,j
0 1 2 3 4 5 6 7 8 9 10
x 10
4
−0.1
−0.05
0
0.05
(e)
ε
i,j
Time [step]
Figure 7: (a) Measured levels x
c
(continuous line), x
a
(dot-
ted line) and x
f
(dashed-dotted line), (b) Euclidienne dis-
tance d
Eu
(M
e
,M
n
), (c) correlation indicators K
c,a
(continu-
ous line), K
c, f
(dashed line) and K
a, f
(dashed-dotted line),
(d) quadratic error indicators ε
2
c,a
(continuous line), ε
2
c, f
(dashed line) and ε
2
a, f
(dashed-dotted line), (e) error in-
dicators ε
c,a
(continuous line), ε
c, f
(dashed line) and ε
a, f
(dashed-dotted line), measured on 2006.
der to reach these objectives. The technique of super-
vision which is presented in this article is based on
the Pattern recognition AUDyC algorithm. It has the
advantage to limit physical knowledge of the system,
and aims to modelling the operating modes of dynam-
ical systems using only measured data. The charac-
teristics of the operating mode are updated in real-
time in order to follow the drifts due to sensor faults,
and detect setting errors and measurement or trans-
mission errors. The proposed technique is applied on
a real hydrographical system with presents the partic-
ularities to not being modelled according to classical
modelling methods. Fault indicators are determined
according to levels which are measured since 2006.
The first obtained results highlight the efficiency of
the proposed fault detection method. However, these
results have to be improved. The futur purposes con-
sist in proposing more pertinent fault indicators by
considering the measured upstream and downstream
in the Cuinchy-Fontinettes channel. It should be also
interesting to take into account the unknown inputs
which correspond to overflows in the channel. In fu-
ture works, a prognosis approach will be proposed to
predict the future state of the level sensors in order to
detect as soon as possible sensor faults. Finally, an
implementation of the proposed technique on the real
system may be considered at term.
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