NEURAL NETWORKS WITH DECISION TREES FOR DIAGNOSIS ISSUES

Sundarapandian et al. (Eds) : CSE, CICS, DBDM, AIFL, SCOM - 2013
pp. 29–39, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3304
NEURAL NETWORKS WITH DECISION TREES
FOR DIAGNOSIS ISSUES
Yahia Kourd1
, Dimitri Lefebvre2
and Noureddine Guersi3
1
Med Khider Biskra University, Department of Electrical Engineering, Algeria
ykourd@ yahoo.fr
2
GREAH – University of Havre 25 rue Philippe Lebon – 76058 Le Havre –
France dimitri.lefebvre@univ-lehavre.fr
3
Badji Mokhtar University, Department of Electronic, Annaba, Algeria
guersi54@yahoo.fr
ABSTRACT
This paper presents a new idea for fault detection and isolation (FDI) technique which is
applied to industrial system. This technique is based on Neural Networks fault-free and Faulty
behaviours Models (NNFMs). NNFMs are used for residual generation, while decision tree
architecture is used for residual evaluation. The decision tree is realized with data collected
from the NNFM’s outputs and is used to isolate detectable faults depending on computed
threshold. Each part of the tree corresponds to specific residual. With the decision tree, it
becomes possible to take the appropriate decision regarding the actual process behaviour by
evaluating few numbers of residuals. In comparison to usual systematic evaluation of all
residuals, the proposed technique requires less computational effort and can be used for on line
diagnosis. An application example is presented to illustrate and confirm the effectiveness and
the accuracy of the proposed approach.
KEYWORDS
Neural Network, Fault Detection and Isolation, Faulty Model, & Decision Tree
1. INTRODUCTION
In a process, early diagnosis of faults, that might occur, allows performing important prevention
actions. Therefore, fault detection is a crucial task in the automatic control of large system as
manufacturing systems (Diag, 2009). Moreover, it allows avoiding heavy economic losses due to
production stop, replacement of spares parts, etc. The need of performing and reliable developed
methods for the systems diagnosis becomes increasingly pressing. These methods should respects
the following points: (1) Standards and quality improvement; (2) Diagnostic failure to improve
the relationship and (3) The definition of new services as new technologies and economic
interests. Most of the fault diagnosis methods found in the literature are based on linear
methodology or exact models. Models of industrial processes are often very complex. It is
difficult to accurately predict their behavior, especially with corrupted measures, and unreliable
sensors. Therefore, a number of researchers have perceived artificial neural networks as an
alternative way to represent knowledge about faults (Sorsa et al. 1992, Himmelblau 1992, Patton
et al. 1994, Frank 1997, Patton et al. 1999, Calado et al. 2001, Korbicz et al. 2004).
This paper presents a FDI method that generates a large number of residuals depending on the set
of candidate faults. The residuals are analyzed and evaluated according to their mean values. A
decision tree is introduced to manage the residuals evaluation and to decide on line which residual

30 Computer Science & Information Technology (CS & IT)
must be evaluated or not. This technique is validated with the DAMADICS benchmark process, a
European project under which several FDI methods have been developed and compared. This
benchmark is based on industry requirements described in (Bartys et al. 2006). It is used with
different approaches in the purpose of providing a training facility for both industry and
academia. It gains a better understanding for the way in which the various FDI methods can
perform in a realistic control engineering application setting. In the literature, different analytical
FDI approaches have been developed. In (Puig et al. 2006), passive robustness fault detection
method using intervals observers is presented. In (Previdi et al. 2006), authors introduce signal
model based fault detection using squared coherency functions. An actuator fault
distinguishability study is presented in (Koscielny et al. 2006). Several soft computing techniques
used in FDI methods have been developed under the scope of DAMADICS. A data-driven
method in FDI is presented in (Bocaniala et al. 2006), where a novel classifier based on particle
swarm optimization was developed. Group method of data handling (GMDH) neural networks
have been used in (Witczak et al. 2006) for robust fault detection. A computer-assisted FDI
scheme based on a fuzzy qualitative simulation, where the fault isolation is performed by a
hierarchical structure of the neuro-fuzzy networks is presented in (Calado et al. 2006). A neuro-
fuzzy modeling for FDI, involving a hybrid combination of neuro-fuzzy identification and
unknown input observers in the neuro-fuzzy and decoupling fault diagnosis scheme, has been
proposed in (Uppal et al. 2006).
The paper is organized as follows: In section 2 the proposed technique for FDI issues is
presented. This method uses models of faulty and fault-free behaviors with Neural Networks. The
design of decision tree is proposed in section 3 to assist the diagnosis on line with early decision.
Section 4 presents the DAMADICS actuator benchmark and application of our approach. Finally,
in the last section a conclusion about the effectiveness of this approach and future research
directions are presented.
2. NEURAL NETWORKS FAULTY AND FAULT-FREE MODELS
2.1. Fault-free Model
Physical processes are often very complex dynamic systems, having strong non linearities. As a
consequence, knowledge based models are not easy to obtain. Simplifications are essential to
formulate an exploitable model, but they may degrade the accuracy of the mathematical model.
Other problems remain with some model parameters that are not easy to measure or estimate and
that could be variable in time. Another problem lies in the systematic processing of data collected
by sensors.
At this stage, unknown nonlinear systems are considered with input vector U(t) = (ui(t)), i =1,...,q
and output vector Y(t) = (yk(t)), k =1,...,n. The state variables are not measurable. Neural
Networks (NN) are introduced to generate accurate models of the system in normal operating
conditions (Kourd et al., 2008, 2010, 2011). The comparison between the output of the system
and the output Y0’(t) = (y’k0(t)), k =1,...,n, of the NN model gives the error vector E(t) = (ek(t)), k
=1,...,n, with:
ek(t)= yk(t) - y'k0(t) (1)
The Neural Network model is trained with data collected from the fault-free system utilizing
Levenberg-Marquardt algorithm with early stopping that uses three data sets (training, testing and
validation) to avoid overfitting. Moreover, this algorithm is known in its fast convergence. The
obtained model is then tested and validated again with other sets of data. In order to get the best
model, several configurations are tested according to a trial error processing that uses pruning

Computer Science & Information Technology (CS & IT) 31
methods to eliminate the useless nodes.
2.2. Faulty Models
When multiple faults are considered, the isolation of the detected faults is no longer trivial and
early diagnosis becomes a difficult task. One can multiply the measurements and use some
analysis tools (residuals analysis) in order to isolate them. In particular, a history of collected data
can be used to improve the knowledge about the faulty behaviors. This knowledge is then used to
design models of faulty behaviors and additional residuals. Such models will be used to provide
estimations for each fault candidate. The decision results are then provided from the comparison
between estimations with the measurements collected during system operations.
The design of faulty models is similar to the method described in previous section (2.A). The
learning of faulty behaviors is obtained according to the Levenberg-Marquardt algorithm with
early stopping. Each model is built for a specific fault candidate fj that is considered as an
additional input. The vectors Y’j(t) = (y’kj(t)), k =1,...,n, j = 1,…p stand for the outputs of the
Neural Networks models designed for the faults fj, j = 1,…p.
3. DECISION TREES FOR RESIDUAL EVALUATION
3.1. Fault detection technique
During monitoring, the direct comparison of the system’s outputs Y(t) and the outputs Y'0(t) of
fault-free model leads to residuals R0(t) = (rk0(t)) and k = 1,…,n with:
rk0(t)= yk(t)-y'k(t), k =1,…,n. (2)
The residual R0(t) provides information about faults for further processing. Fault detection is
based on the evaluation of residuals magnitude. It is assumed that each residual rk0(t), k = 1,…,n
should normally be close to zero in the fault-free case, and it should be far from zero in the case
of a fault. Thus, faults are detected by setting threshold Sk0 on the residual signals (Fig. 1 up,
single residual and a single fault are considered for simplicity). The analysis of residuals rk0(t)
also provides an estimate τk of the time of occurrence tf used for diagnosis issue. When several
residuals are used, the estimate τ of the time of occurrence of faults is given by:
τ = min {τk, k = 1,…,n} (3)
Figure 1. Fault detection by thresholding technique
The faults are detected when the magnitude of one residual |rk0(t)| augments the threshold Sk0:
൜
|‫ݎ‬௞଴ሺ‫ݐ‬ሻ|≤ ܵ௞଴: No fault is detected at time ‫ݐ‬
|‫ݎ‬௞଴ሺ‫ݐ‬ሻ| > ܵ௞଴: A fault is detected at time ‫ݐ‬
(4)
Fault occurrence f
False alarm r(t)
-
0
S0
0 t
t
f(t)
τ
Delay
Fault f detected at
instant τ
t f

The main difficulty with this evaluation is that the measurement of the system outputs yk(t) is
usually corrupted by disturbances (for example, measurement noise). In practice, due to the
modeling uncertainties and disturbances, it is necessary to assign large thresholds Sk0 in order to
avoid false alarms. Such thresholds usually imply a reduction of the fault detection sensitivity and
can lead to non detections. In order to avoid such problems, one can run also the models of faulty
behaviors from t=0 and use the method described below. The idea is to evaluate the probability of
the fault candidates at each instant. A fault is detected when the probability of one neural network
faulty model NNFM(j), j = 1,..., p becomes larger than the probability of the fault-free model
NNFM(0) (Kourd et al., 2010).
3.2. Fault diagnosis based on three valued residuals
The proposed approach is based on the analysis of the outputs obtained after applying the input
U(t) on the real system. Obtained outputs are also analyzed in parallel with fault-free and faulty
behaviors models constructed by Neural Networks technique (Kourd et al., 2012). Detection and
diagnosis are achieved from residuals generation Rj(t), j = 0,...,p according to a decision block.
The diagnosis results either from the usual thresholding technique or from the on-line
determination of fault probabilities and confidence factors (Kourd et al., 2011). In the second
method, the faulty models run simultaneously from time t = τ where τ is the time when the fault
is detected. Each model will behave according to a single fault candidate and the resulting
behaviors will be compared with the collected data to provide rapid diagnosis. In case of
numerous fault candidates fj, j = 1,…,p, the output Y'j(t) = (y’k(t, fj,τ)) of the model NNFM(j) is
compared with the measured vector Y(t) to compute additive residual Rj(t)= (rkj(t,τ)), k = 1,…,n.
The most probable fault candidate is determined according to the comparison of all residuals
rkj(t,τ), k = 1,…,n, j = 1,…,p resulting from the n outputs and p models of faults:
‫ݎ‬௞௝ሺ‫,ݐ‬ ߬ሻ = ‫ݕ‬௞ሺ‫ݐ‬ሻ − ‫ݕ‬௞௝
ᇱ
ሺ‫,ݐ‬ ߬ሻ (5)
According to the residual analysis of rkj obtained by equation (5), and adopting positive and
negative thresholds, threes values of these residuals are obtained (positive, negative or null). The
comparison of the current residual with the signatures matrix leads to diagnosis (Chen et al.
1999).
3.3. Decision trees for residual evaluation
The aim of this section is to propose a hierarchical structure to simplify the diagnosis of
automation systems when numerous residuals are computed. The idea is to organize the residuals
in a decision tree. This tree is used to compute only the selection of residuals that are the most
significant for the current signal. The tree starts with the evaluation of the residual that
corresponds to the fault free model (step 1). The value of the residual is used to classify the fault
candidates in several subgroups (figure 2, G1 to Gm). Each subgroup limits the number of fault
candidates. The algorithm continues by evaluating second residual that depends on the subgroup
resulting from the first step (step 2). Another time the fault candidates are separated into
subgroups (figure 2, G11 to G1t for example).

Figure 2. FDI decision tree proposed
According to that evaluation, the algorithm continues until a subset of faults with a single
candidate is isolated (presented by the leaves of tree in figure 2). Finally the faults are isolated by
the computation of selected residuals. Thus the computational effort is reduced in comparison
with the systematic evaluation of all residuals. In practice, the use of hierarchical architecture is
feasible online or offline depending on the complexity of the system.
4. APPLICATION IN INDUSTRIAL SYSTEM
To evaluate FDI methods proposed, The DAMADICS benchmark is an engineering research
case-study that can be used. This is electro-pneumatic valve actuator in the Lublin sugar factory
in Poland (Bartys et al., 2006). Its main characteristics are:
(a) The DAMADICS benchmark is based on the physical phenomena that give origin to faults in
the system.
(b) The DAMADICS benchmark clearly defines the process and data sets; the fault scenarios are
standardized. This is done in view of industrial applicability of the tested FDI solutions, to cut off
methods that have no practical feasibility.
4.1. Actuator description
The actuator consists of a control valve, a pneumatic servomotor and a positioner (Figure 3). In
the actuator, faults can appear in: control valve, servo-motor, electro-pneumatic transducer, piston
rod travel transducer, pressure transmitter or microprocessor control unit. A total number of 19
different faults is considered (p = 19, Table 1). The faults are emulated under carefully monitored
conditions, keeping the process operation within acceptable limits. Five available measurements
and one control value signal have been considered for benchmarking purposes: process control

external signal (CV), liquid pressures on the valve inlet (P1) and outlet (P2), liquid ﬂow rate (F),
liquid temperature (T1) and servomotor rod displacement (X).
Figure 3. Electro-pneumatic valve Schema.
Within the DAMADICS project the actuator simulator was developed under MATLAB Simulink.
This tool makes it possible to generate data for the normal and 19 faulty operating modes. The
considered faults can be either of abrupt or incipient origins. Abrupt faults are of small (S),
medium (M) or big (B) magnitude.
4.2. Neural Network Fault-free Model for Actuator
Two Multi Layer Perceptron (MLP) neural networks are designed to model the outputs y1(t) =
X(t) and y2(t) = F(t) of the DAMADICS system in case of fault-free behaviors. y’10(t) = X’(t) and
y’20(t) = F’(t) are the estimated values of X(t) and F(t) processed by NNs:
(X’, F')= NNFM(0) {CV, P1, P2, T1, X, F} (6)
Where NNFM(0) stands for the double MLP structures. To select the structure of NNFM(0),
several tests are carried out to obtain the best architectures (with minimal number of hidden layers
and number of neurons by layer) for modeling the operation of the actuator. The training, testing
and validating data is simulated using Matlab Simulink actuator model. Validation is done by the
measured data provided by 'Lublin Sugar Factory'.
4.3. Neural Network faulty Model for Actuator
The preceding method is applied to build NNs models corresponding to the 19 fault candidates
that are considered in DAMADICS benchmark. For that purpose, it is necessary to create a data
base that contains samples for all faults (Kourd et al., 2011) exposed to the DAMADICS system.
The method is illustrated in Figure 4 for the fault fj with j=1 to 19 faults. The network NNFM(j)
learns the mapping from q=6 inputs to n=2 outputs when fault fj is assumed to affect the system
from time t = 0. Equation (7) holds:
(X’j, F'j)= NNFM(j) (CV, P1, P2, T1, Xj, Fj) (7)
To select the structure of NNFM(j), numerous tests are carried out to obtain the best architectures.
The training and test data are generated using Matlab-Simulink DABLIB models (DAMADICS
2002). The best structure is a Neural Network with 6 nodes in the first hidden layer, 3 nodes in
the second hidden layer and two output neurons. Validation is done with the measured data
provided by the Lublin Sugar Factory in 2001 (DAMADICS 2002).
S
DT
FT
E/P PC
Positioner
CVIPs
V
V3
V1 V2
F
SP
X
PSP
Pz
Pneumatic servo
motor
Valve
PTPT
P1 P2
Fv3
FvT1
TT
P
X
D/APV
Hydraulic
pipes
S
PT

Figure 4. Schema of Neural Networks faulty model NNFM(j).
4.4. Residual by Three valued for Actuator system:
The residuals analysis is an essential step in FDI systems. The first parameter to be defined in the
design of a fault detection system is the threshold value, which value allows the system to meet
the required false alarm probability. The problem of the threshold selection is closely linked to the
behavior of residuals and also to constraints that may be imposed such as security margins
tolerance (Lefebvre et al., 2010). The residual vector R0(t) = (rk0(t)), k = 1, 2 for DAMADICS
actuator is first considered for fault detection:
ቊ
‫ݎ‬ଵ଴ሺ‫ݐ‬ሻ = ܺሺ‫ݐ‬ሻ − ܺ′ሺ‫ݐ‬ሻ
‫ݎ‬ଶ଴ሺ‫ݐ‬ሻ = ‫ܨ‬ሺ‫ݐ‬ሻ − ‫ܨ‬′ሺ‫ݐ‬ሻ
(8)
Where X’ and F’ are the outputs of the NN model of fault-free behaviors. The detection is
obtained by comparing residuals with appropriate thresholds. Three-valued signal are obtained
(positive, negative and zero). The thresholds are figured out according to the standard deviation
of the residual for fault-free case (Kourd et al., 2011). Let us notice that the choice of constant or
adaptive thresholds strongly influences the performance of the FDI system. The thresholds must
be thoroughly selected. For the continuation of our work, the thresholds S10=5*σ1 = 0.0027 and
S20 =5*σ2 =0.004 are selected where σ1 and σ2 are the standard deviations obtained from the
learning process. Table 1 sums up the detection performances for the 19 types of faults according
to the sign of the residual vector R0.
Table 1 Signature matrix for DAMADICS Actuator with residual R0
G0 f0 f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 f11 f12 f13 f14 f15 f16 f17 f18 f19
r10 0 +1 -1 0 -1 0 0 +1 0 0 +1 -1 -1 -1 0 -1 +1 +1 0 0
r20 0 -1 +1 -1 -1 +1 -1 -1 0 -1 -1 +1 +1 -1 0 +1 -1 -1 -1 +1
The signification of (+1) is the case where the residual is above the positive threshold, (-1) is the
case where the residual is below the negative threshold and (0) when the residual is between both
thresholds.

Figure 5. Residuals r10 and r20 in the case where f1 was simulated during time interval [350s
1000s].
The evaluation of residual vector R0 leads to a first stage in detection and isolation. From Table 1,
six groups of faults with similar symptoms can be separated:
• Group 1 : ‫ܩ‬ଵ = ሼ݂ଷ ݂଺ ݂ଽ ݂ଵ଼ሽ with signature ൫ ଴
ିଵ
൯
• Group 2 : ‫ܩ‬ଶ = ሼ݂ଵ ݂଻ ݂ଵ଴ ݂ଵ଺ ݂ଵ଻ሽ with signature ൫ାଵ
ିଵ
൯
• Group 3 : ‫ܩ‬ଷ = ሼ݂ହ ݂ଵଽሽ with signature ൫ ଴
ାଵ
൯
• Group 4 : ‫ܩ‬ସ = ሼ݂ଶ ݂ଵଵ ݂ଵଶ ݂ଵହሽ with signature ൫ିଵ
ାଵ
൯
• Group 5 : ‫ܩ‬ହ = ሼ݂ସ ݂ଵଷሽ with signature ൫ିଵ
ିଵ
൯
• Group 6 : ‫ܩ‬଺ = ሼ݂଴ ଼݂ ݂ଵସሽ with signature ൫଴
଴
൯
The faults in groups G1 to G5 are detected but not isolated because the signatures over r10 and r20
are similar within the group. One can also notice that the faults in group G6 have the same
signature as the fault-free behaviors. Thus faults in group G6 cannot be directly detected with
residuals r10 and r20.
For this reason other residuals generated by faulty behavior neural models NNFM(j) can be added
for each group. This allows the building of signatures matrix. An evaluation step that uses same
thresholding technique is followed up. Tables 2 to 7 sum up the detection performances for the 19
types of faults according to the sign of the residual vector Rj(t) generated by NNFM(j) with j=
1,...,19.
400 500 600 700 800 900 1000
-0.01
-0.005
0
0.005
0.01
400 500 600 700 800 900 1000
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
Mean value
of r10
Mean value
of r20
r10
r20
t
t
+/-S10
+/-S20

Table 2 Table 3 Table 4
Signature matrix for group G1 Signature matrix for group G2 Signature matrix for group G3
Table 7
Table 5 Signature matrix for group G6
Signature matrix for group G4 Table 6
Signature matrix for group G5
4.5. Decision tree for diagnosis DAMADICS Actuator:
The introduction of probabilities to evaluate the significance of each residual and the reliability of
the decision is another component of our approach. The proposed method uses a time window
that can be sized according to the time requirement. Diagnosis with a large time window includes
a diagnosis delay but will lead to a decision with a high confidence index. On the contrary single
diagnosis with a small time window leads to early diagnosis but with a lower confidence index.
The diagnosis results either from the usual thresholding technique or from the on-line
determination of fault probabilities and confidence factors (Kourd et al., 2011).
According to the values of residuals and selection of faulty model NNFM (j), we introduce
decision trees to set the path of the branch that must be followed for the isolation of all detected
faults. From this standpoint, we can configure many decision trees that lead to various
computational complexities.
5. CONCLUSION
The accurate and timely fault diagnosis of safety critical systems is important because it can
decrease the probability of catastrophic failures, increase the life of the plant, and reduce
maintenance costs. In this paper, a multiple-model FDI scheme is presented for a DAMADICS
actuator. NNs technique is applied for residual generation. In the first step of FDI, the use of NNs
method is considered as an alternative to the traditional model-based approach for residual
generation. When quantitative models are not readily available, a correctly trained NNs model
can be used as a non-linear dynamic model of the system. The proposed NNFMs presented in this
paper can be used to diagnose the faults in the DANMADICS actuator. In the second step of the
FDI task, a decision tree was introduced for residual evaluation. An evolutionary technique was
used to isolate the faults step by step by separating the fault candidates into several groups with
same signatures. This study shows that the developed approach can produce good diagnosis
results for a complex system exposed to numerous faults. The use of decision trees divides the
computational effort by 10 for the DAMADICS system and can be implemented on line.
G1 f3 f6 f9 f18
r13 0 0 0 0
r23 0 -1 -1 -1
r16 0 0 0 0
r26 -1 0 -1 -1
r19 +1 +1 0 +1
r29 -1 +1 0 +1
r118 0 0 0 0
r218 -1 -1 -1 0
G2 f1 f7 f10 f16 f17
r11 0 +1 +1 +1 +1
r21 0 0 -1 -1 -1
r17 -1 0 +1 +1 +1
r27 0 0 -1 -1 -1
r110 -1 -1 0 +1 +1
r210 +1 +1 0 -1 -1
r116 -1 -1 -1 0 +1
r116 +1 +1 +1 0 -1
r117 -1 -1 -1 -1 0
r117 +1 +1 +1 +1 0
G3 f5 f19
r15 0 0
r25 0 -1
r119 0 0
r219 1 0
G4 f2 f11 f12 f15
r12 0 +1 +1 +1
r22 0 +1 +1 -1
r111 -1 0 +1 +1
r211 -1 0 -1 -1
r112 -1 -1 0 +1
r212 -1 +1 0 -1
r1115 -1 -1 -1 0
r215 +1 +1 +1 0
G5 f4 f13
r14 0 0
r24 0 +1
r113 -1 0
r213 -1 0
G6 f0 f8 f14
r10 0 0 0
r20 0 0 0
r18 0 0 0
r28 0 0 0
r114 0 0 0
r214 0 0 0

Applying this scheme on other types of operating systems will be the key issue of our interests in
future research, in which other labeled faults will be investigated. This work can be extended to
diagnose new faults using integrated set of decision tree which looks to be a worthwhile direction
for future case study. Implementing this decision tree for the online diagnosis issues as well as
choosing its components is an open task for more optimization and quick isolation.
ACKNOWLEDGMENTS
The work was supported in part by Ministry of Higher Education and Scientific Research in
Algeria.
REFERENCES
[1] Bartys M., Patton R., M. Syfert, Heras S. & Quevedo J., (2006) "Introduction to the DAMADICS FDI
benchmark actuator study". Control engineering practice. 14. 577-596.
[2] Brown M. & Harris C. J., (1994) "Neuro-fuzzy adaptive modelling and control". Englewood Cliffs,
NJ: Prentice-Hall.
[3] Browne A., Hudson B. D., Whitley D. C., Ford M.G. & Picton P., (2004) "Biological data mining
with neural networks: implementation and application of a flexible decision tree extraction algorithm
to genomic problem domains". Neurocomputing. 57. 275–293.
[4] Calado J., Korbicz J., Patan K., Patton R.J & Sa da Costa J., (2001) "Soft computing approaches to
fault diagnosis for dynamic systems". European Journal of Control 7. 248–286.
[5] Calado J., & Sa da Costa J., (2006) "Fuzzy neural networks applied to fault diagnosis. In:
Computational intelligence in fault diagnosis", Springer-Verlag, London.
[6] Chen J. & Patton R.J., (1999) "Robust Model-Based Fault Diagnosis for Dynamic Systems". Kluwer
Academic Publishers, Berlin.
[7] Chen Y., Abrahama A. & Yanga B., (2006) "Feature selection and classification using flexible neural
tree". Neurocomputing. 70. 305–313.
[8] DAMADICS, (2002) Website of the RTN DAMADICS. (http://diag.mchtr.pw.edu.pl/damadics).
[9] Frank P.M. & Koppen-Seliger B., (1997) "New developments using AI in fault diagnosis".
Engineering Applications of Artiﬁcial Intelligence 10. 3-14.
[10] Gertler, J. (1998) "Fault detection and diagnosis in engineering systems", New York, Basel, Hong
Kong: Marcel Dekker.
[11] Guglielmi G., Parisini T. & Rossi G., (1995) "Fault diagnosis and neural networks: A power plant
application" (keynote paper). Control Engineering Practice. 3. 601–620.
[12] Himmelblau D. M., (1992) "Use of artiﬁcial neural networks in monitor faults and for troubleshooting
in the process industries". In: Preprints IFAC Symp. On-line Fault Detection and Supervision in the
Chemical Process Industries, Newark, Delaware. USA. 144-149.
[13] Isermann R., (1994) "Fault diagnosis of machines via parameter estimation and knowledge
processing", A tutorial paper. Automatica. 29. 815–835.
[14] Isermann R., (1997) "Supervision, fault detection and diagnosis of technical systems". Special Section
of Control Engineering Practice 5.
[15] Isermann R., (2006) "Fault Diagnosis Systems. An Introduction from Fault Detection to Fault
Tolerance". Springer-Verlag, New York.
[16] Janczak A., (2005) "Identification of Nonlinear Systems Using Neural Networks and Polynomial
Models". A Block-oriented Approach. Lecture Notes in Control and Information Sciences. Springer-
Verlag, Berlin.
[17] Jang J. S. R., Sun C. T. & Mizutani E., (1997) "Neuro-Fuzzy and Soft Computing". Englewood Cliffs,
NJ: Prentice-Hall.
[18] Korbicz J., Koscielny J., Kowalczuk Z. & Cholewa W., (2004) "Fault Diagnosis. Models, Artificial
Intelligence, Applications". Springer-Verlag, Berlin Heidelberg.
[19] Korbicz J., Patan K. & Kowal M., eds., (2007) "Fault Diagnosis and Fault Tolerant Control.
Challenging Problems of Science - Theory and Applications: Automatic Control and Robotics".
Academic Publishing House EXIT, Warsaw.
[20] Koscielny J. M., Bartys M., Rzepiejewski P. & S. da Costa J., (2006) "Actuator fault
distinguishability study for the DAMADICS benchmark problem". Control Engineering Practice, 14,
645-652.

[21] Kourd Y., Guersi N. & Lefebvre D., (2008) "A two stages diagnosis method with Neural networks".
Proceeding ICEETD, Hammamet Tunisie.
[22] Kourd Y., Guersi N. & Lefebvre D. (2010), "Neuro-fuzzy approach for fault Diagnosis: application to
the DAMADICS". 7th international Conference on Informatics in Control, Automation and Robotics.
Proceeding ICINCO 2010. Funchal. Portugal.
[23] Kourd Y., Lefebvre D. & Guersi N. (2011) "Early FDI Based on Residuals Design According to the
Analysis of Models of Faults: Application to DAMADICS". Advances in Artificial Neural Systems.
doi:10, 1155/2011/453169.
[24] Lefebvre D., Chafouk H. & Lebbal M. (2010) "Modélisation et Diagnostic des Systèmes. Une
Approche Hybride". Editions Universitaires Européennes.
[25] Maji P., (2008) "Efficient design of neural network tree using a new splitting criterion".
Neurocomputing. 71. 787-800.
[26] Narendra K., Balakrishnan J. & Kermal M., (1995) "Adaptation and learning using multiple models,
switching and tuning". IEEE Control Systems Magazine. 37–51.
[27] Osowski S. (1996) "Neural Networks in Algorithmitic Expression". WNT, Warsaw (in Polish).
[28] Patan K. & Parisini T., (2005) "Identification of neural dynamic models for fault detection and
isolation: The case of a real sugar evaporation process". Journal of Process Control. 15.67-79
[29] Patton R.J., Chen J. & Siew T. (1994) "Fault diagnosis in nonlinear dynamic systems via neural
networks". In: Proc. of CONTROL’94, Coventry, UK. 2. 1346–1351.
[30] Patton, R.J., Korbicz, J., (1999) "Advances in computational intelligence". Special Issue of
International Journal of Applied Mathematics and Computer Science 9.
[31] Patton R.J., Frank P.M. & Clark R., (2000) "Issues of Fault Diagnosis for Dynamic Systems".
Springer-Verlag, Berlin.
[32] Pomorski D., Perche P. B., (2001) "Inductive learning of decision trees: application to fault isolation
of an induction motor". Engineering Applications of Artificial Intelligence. 14,155–166
[33] Previdi F. & Parisini T., (2006) "Model-free actuator fault detection using a spectral estimation
approach: the case of the DAMADICS benchmark problem". Control Engineering Practice. 14, 635–
644.
[34] Sorsa T. & Koivo H. N., (1992) "Application of neural networks in the detection of breaks in a paper
machine". In: Preprints IFAC Symp. On-line Fault Detection and Supervision in the Chemical Process
Industries, Newark, Delaware, USA. 162–167.
[35] Sugumaran V., Muralidharan V. & Ramachandran K. I., (2007) "Feature selection using decision tree
and classification through proximal support vector machine for fault diagnosis of roller bearing".
Mechanical Systems and Signal Processing. 21. 930-942.
[36] Uppala F. J., Patton R. J. & Witczak M., (2006) "A neuro-fuzzy multiple-model observer approach to
robust fault diagnosis based on the DAMADICS benchmark problem". Control Engineering Practice.
14. 699-717.
[37] Witczak M. & Korbicz J., (2002) "Genetic programming in fault diagnosis and identification of non-
linear dynamic systems". In: W. Cholewa, J. Korbicz, Koscielny & J.M. Kowalczuk, (Eds.),
Diagnostics of processes. Models, Methods of Artifical Intelligence, Applications, Scientific
Engineering Press, Warsaw.
[38] Witczak M., (2007) "Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear
Systems". From Analytical to Soft Computing Approaches. Lecture Notes in Control and Information
Sciences. Springer–Verlag, Berlin.
[39] Witczak M., Korbicz J., Mrugalski M. & Patton R. J., (2006) "A GMDH neural network-based
approach to robust fault diagnosis: Application to the DAMADICS benchmark problem". Control
Engineering Practice. 14. 671-683.
[40] Zhang K., Li Y., Scarf P. & Ball A., (2011) "Feature selection for high-dimensional machinery fault
diagnosis data using multiple models and Radial Basis Function networks". Neurocomputing. 74.
2941–2952.

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