Computers in Industry 57 (2006) 552–568
www.elsevier.com/locate/compind
SIMAP: Intelligent System for Predictive Maintenance
Application to the health condition monitoring of a windturbine gearbox
Mari Cruz Garcia a, Miguel A. Sanz-Bobi a,*, Javier del Pico b
a
Universidad Pontificia Comillas, Instituto de Investigación Tecnológica, IIT, Santa Cruz de Marcenado 26, 28015 Madrid, Spain
b
Molinos del Ebro S.A. Paseo de la Independencia 21, 38 50001 Zaragoza, Spain
Accepted 9 February 2006
Available online 13 June 2006
Abstract
SIMAP is the abbreviated name for the Intelligent System for Predictive Maintenance. It is a software application addressed to the diagnosis in
real-time of industrial processes. It takes into account the information coming in real-time from different sensors and other information sources and
tries to detect possible anomalies in the normal behaviour expected of the industrial components. The incipient detection of anomalies allows for an
early diagnosis and the possibility to plan effective maintenance actions. Also, the continuous monitoring performed allows for an estimation in a
qualitative form of the health condition of the components. SIMAP is a general tool oriented to the diagnosis and maintenance of industrial
processes, however the first experience of its application has been at a windfarm. In this real case, SIMAP is able to optimize and to dynamically
adapt a maintenance calendar for a monitored windturbine according to the real needs and operating life of it as well as other technical and
economical criteria. In particular this paper presents the application of SIMAP to the health condition monitoring of a windturbine gearbox as an
example of its capabilities and main features.
# 2006 Elsevier B.V. All rights reserved.
Keywords: Predictive maintenance; Maintenance effectiveness; Health condition; Diagnosis; Artificial intelligence
1. Introduction
The use of wind is one of the most attractive new sources of
energy at the present moment, as can be seen by the growing
installation of windfarms all over the world. Windturbines are
relatively young machines where the application of a correct
maintenance strategy would be very important for the protection
of their future life, productivity and profitability [1]. The current
practice of maintenance applied to the existing aerogenerators is
based on periodical or preventive maintenance actions recommended by their manufacturers. These are good and general
guidelines for the maintenance of aerogenerators, however they
do not focus on the specific characteristics of the real and local
life of them such as: weather conditions at the location, stress by
over-load, hours continuously working, etc. These factors
determine the particular life and health of each aerogenerator
and for this reason the maintenance applied has to also take them
into account. In order to do this, a predictive maintenance plan is
* Corresponding author. Tel.: +34 91 542 28 00x4240; fax: +34 91 542 31 76.
E-mail address: masanz@upcomillas.es (M.A. Sanz-Bobi).
0166-3615/$ – see front matter # 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.compind.2006.02.011
the best option to guarantee the long life of the new investments
in aerogenerators due to the maintenance actions which are
applied according to the real and specific health conditions of
every aerogenerator during its life and not only based on general
guidelines.
When thinking about a predictive maintenance strategy for
aerogenerators, it is important to remark that windturbines are
quite new machines using an important number of sensors able
to supply information to different controllers in order to
perform the best control and efficient operation of them. The
information collected by the sensors of aerogenerators for
control purposes can also be used for monitoring the health
condition of their different components and to apply a
predictive maintenance plan. According to this, no new
investment in sensors is required in order to perform an
effective windturbine predictive maintenance strategy because
all the aerogenerators include a set of sensors from the
manufacturer for different aspects of the control of their
elements. The information from these sensors can also be used
as main information source for a predictive maintenance plan.
This paper presents the architecture of a new predictive
maintenance system, called SIMAP, based on artificial
M.C. Garcia et al. / Computers in Industry 57 (2006) 552–568
intelligent techniques. Its predictive maintenance strategy can
be applied to any industrial system or equipment and its main
goal is to find the most appropriate time to carry out the needed
maintenance actions both from a component health condition
and an incipient failure diagnosis perspectives. The new and
positive aspects of this predictive maintenance methodology
have been tested in windturbines. SIMAP is able to create and
dynamically adapt a maintenance calendar for the windturbine
that it is monitoring. The criteria followed are set up according
to the real needs and operating life of the windturbine. This
process is performed on-line and is different from the
traditional scheduled maintenance plan based on fixed time
intervals following the manufacturer criteria which do not focus
on the real operation conditions of the aerogenerators.
According to this, SIMAP implements the main aspects of
an e-maintenance approach in a computer network such as local
and remote continuous monitoring and diagnosis of the main
components of the aerogenerators, maintenance actions
planned according to the current and historical information
collected, distribution of the on-line diagnosis and maintenance
workload in different modules interconnected through a
computer network and finally, different levels of warnings
for the operator. This predictive maintenance system has been
developed and applied to a windfarm owned by a Spanish wind
energy company called Molinos del Ebro, S.A.
This paper provides in Section 2 an overview of the main
features and architecture of SIMAP and, in Sections 3–9,
presents the capabilities of SIMAP applied to a particular case
of a windturbine, which is the possible failures in a gearbox and
how SIMAP works in real-time to detect and diagnose
anomalies in this component, to evaluate its health condition
and to plan predictive maintenance actions.
2. SIMAP: objectives and architecture
The principal tasks performed by SIMAP are the following:
Continuous collection of data coming from different sensors
installed in the aerogenerator and meteorological towers.
Continuous processing of the information collected in order
to evaluate on-line the health condition of the aerogenerator
components and also to detect if some symptoms of
degradation or anomalies are present or could become
present [3]. Both health condition evaluation and incipient
fault detection are based on normal behaviour modelling (that
is, in absence of failures) of the aerogenerator components.
Thus, previously normal behaviour models were obtained
using real data in order to characterize the normal dynamics
of the representative variables of each component without
any failure, taking into account both the different operation
conditions of the components. SIMAP is working on-line
taking current measurements from the process and evaluating
the prediction of values from the models. The comparison
between measured and predicted values of particular
variables permits the incipient fault detection and the health
condition evaluation for:
a. Diagnosis of the root causes of the symptoms detected.
553
b. Failure risk forecasting of the aerogenerator components
according to their actual health condition.
c. Dynamical maintenance scheduling based on the machine
condition, its environmental conditions and the aerogenerator production plan. Maintenance scheduling pursues
to interfere the least possible with the production plan in
order to maximize the aerogenerator availability and, also,
to minimize the maintenance costs required. Other
technical criteria considered are:
- the failure risk of the aerogenerator components,
estimated on-line based on their health condition;
- the criticality of the components;
- the maintenance actions efficiency to solve or mitigate
the failure or degradation diagnosed;
- the variable maintenance resources as well as the
different relations among maintenance actions (precedence relations, compatibility relations, etc.).
Effectiveness of the maintenance actions applied according to the change observed in the health condition and
degradation of the components affected by these maintenance
actions. This measurement will allow for both technical and
economical comparisons of possible different maintenance
strategies to be applied, as well as maintenance actions
performed during different time periods.
These tasks are organized in a modular architecture
presented in Fig. 1 around the following six main modules:
- Normal Behaviour Models. These models are able to predict
on-line the normal behaviour (or reference behaviour)
expected for each windturbine component, according to its
current working and environmental conditions. These models
are created mainly by means of artificial neural networks due
to their ability to model dynamic non-linear industrial
processes [4,5].
- Anomalies Detection Module. Its main goal is to detect
possible anomalies in components by means of the results
given by the normal behaviour models. Thus, by comparing
for each component, its normal behaviour estimation with its
real behaviour, both a normal behaviour deviation degree as
well as an estimation certainty degree are obtained. These are
used to recognize an anomaly present and the certainty of it
[6–10].
- Health Condition Assessment Module. Its function is to
evaluate on-line the health condition of each windturbine
component as well as the general windturbine health
condition. This function is performed by means of the results
given by the normal behaviour models [11].
- Diagnosis Expert Module. Its main goal is to identify the
possible failure modes that are present or developing in a
windturbine component before this component faults in an
irreversible way (for this reason, these detection and diagnosis
tasks are called incipient) [12]. In order to reach this objective,
this module employs a fuzzy expert system [14,15] able to
represent in a flexible way both the knowledge and the
uncertainty involved in this reasoning process, that is, mainly
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Fig. 1. SIMAP architecture.
the windturbine component relations between symptoms
(anomalies and degradations) and failure modes.
- Predictive Maintenance Scheduling Module. Its main goal is
to optimally schedule windturbine maintenance actions,
suitable to avoid or mitigate the detected incipient failures
as well as the measured component degradation. This
scheduling is optimal according to several technical and
economical criteria which were previously mentioned. For the
scheduling task, this module employs a fuzzy genetic
algorithm [16–18] due to its ability to perform real and
large-scale dynamical multi-objective non-linear optimizations with variable constraints. Fuzzy logic is employed in
order to adequately represent maintenance tasks costs and
duration uncertainties [19,20].
- Maintenance Effectiveness Assessment Module. Its main
function is to obtain an effectiveness measurement for each
maintenance action applied. Consequently, it allows for the
assessment of the maintenance convenience from a technical
and economical viewpoint. As an example, this index is
calculated by means of measuring the aerogenerator gearbox
health condition change before and after applying a
maintenance action (see Fig. 2):
efficiency ¼
through the corresponding models of normal behaviour in order
to estimate its predicted normal behaviour according to the
current working conditions. Once this is completed, both predicted and observed new sets of values are passed in parallel
through the anomalies detection module and the health con-
T
ðtanh ðai2 Þ tanh ðai1 ÞÞ
2T max
According to Fig. 1, every new set of real measurements taken
by the data acquisition system of an aerogenerator, is passed
Fig. 2. Maintenance task efficiency calculation.
M.C. Garcia et al. / Computers in Industry 57 (2006) 552–568
555
Optimization of aerogenerators life cycle by applying a
maintenance strategy that pretends to delay or reduce
components degradation.
Actual effectiveness measurement of maintenance actions
applied, information that is important for getting the best
scheduling of maintenance actions that have been proved to
effectively solve or mitigate particular components degradations or incipient failures.
All of the SIMAP modules have been developed in C++
language for MS-Windows operative system. Different data
files contain the information required from various modules of
SIMAP. The historical information collected by SIMAP is also
stored in a set of files. The neural network models are based on
multiplayer perceptrons. They were trained and tested using the
neural network toolbox of MATLAB and finally they were
integrated with the rest of the C++ code of SIMAP.
Fig. 3. Gearbox and oil cooler system.
dition assessment module. The first module is in charge of
discovering if symptoms of an anomaly of possible failure
mode is present because something does not correspond to the
normal behaviour expected. At the same time and in any case,
the health condition assessment module evaluates the health
condition of each windturbine component. If one or several
anomalies are detected, the Diagnosis Expert Module tries to
identify the causes using experience stored in its knowledge
base. According to the anomalies and causes detected and/or
the health condition of the windturbine components, the predictive maintenance scheduling module will re-plan the maintenance actions to be executed. The aerogenerator
maintenance calendar will be taken into account before being
adapted to the new requirements of e-maintenance. Finally, and
after the application of the predictive maintenance actions, the
maintenance effectiveness assessment module will analyse the
effectiveness of the maintenance performed from a qualitative
point of view.
Another important issue implemented in SIMAP is related to
the automatic self-learning and refinement of the knowledge
and functionality of their previous tasks. This method is based
on the dynamical analysis of the effectiveness measurements of
the maintenance actions applied as well as the health condition
or degradation paths registered along the whole life of the
aerogenerator components. This last information lets SIMAP
characterize the health condition dynamics under degradation
and failure processes and according to this knowledge, to
forecast on-line the components failure time and to plan the best
predictive maintenance strategy to avoid or delay this situation.
The advantages of applying this predictive maintenance
strategy may be stated as follows:
Maintenance intervals are frequently better adapted to the
real needs of the windturbine than when using a preventive
maintenance strategy with fixed maintenance intervals,
because the life of the aerogenerator is taken into account.
It is more cost effective, and provides the most availability,
reliability and security effectiveness.
3. Application of SIMAP to determine the health
condition of a windturbine gearbox
This section, and those that immediately follow, describe a
real application of SIMAP focused on knowing the health
condition of a windturbine gearbox in order to apply a
predictive maintenance scheduling. SIMAP can analyse more
aspects and also, can be applied to more complex components,
however it was preferred to only focus on the evaluation of the
health condition of this simple case as an example.
First the physical system will be briefly described. Fig. 3
shows a diagram of a windturbine gearbox and its oil cooler
system. It includes the physical layout of each component and
the available sensors: gearbox bearing temperature, tank oil
temperature and two digital signals informing if the cooler fan
is operating on a slow or a fast speed mode.
The main purpose of the windturbine gearbox is the
conversion from the rotor slow speed to the electrical generator
fast speed. Furthermore, the generated power of the windturbine is proportional to the wind speed and consequently to
the rotor speed. Thus, the gearbox bearing temperature depends
mainly on several working and environmental conditions:
windturbine generated power, the nacelle temperature and the
cooling phenomenon produced by the cooling system, which
can be measured by the two digital signals of the cooler fans
previously mentioned. Fig. 4 shows the typical temporal
evolution of all these variables monitored on-line in a
windturbine over a period of 2 weeks. The gearbox is one of
the critical components in a windturbine. It is responsible for
around 15–20% of its maintenance costs and also its downtime
[2,24,13]. It is difficult to inspect and in case of replacement, it
takes a great deal of time for a crane on top of the aerogenerator
to dismount and mount the gearbox. In order to reduce these
costs and downtime to the minimum, the strategy of emaintenance based on SIMAP has been applied.
According to the previous information, the behaviour and
health condition of the windturbine gearbox can be described
by three characteristics: gearbox bearing temperature, cooling
oil temperature of the gearbox and the difference between the
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Fig. 4. Temporal evolution of gearbox main variables.
hot and cold temperatures of the oil circulating through the
gearbox cooling circuit.
These three characteristics supply good arguments to do a
qualitative evaluation about the behaviour and health condition
of the windturbine. All the characteristics will be monitored online by three normal behaviour models. In general, in SIMAP
the normal behaviour models are used to predict the evolution
expected for key variables representative of the health
conditions of the components monitored. The models are
fitted before they are used as references of normal behaviour.
They allow for the characterization of the typical relationships
between a set of input variables and one or several output
variables for all the possible working conditions considered as
normal behaviour of the equipment or process monitored. Once
the models are fitted, they can be used in real-time to compare
their estimation to the real values of the variables predicted and,
if a relevant deviation is observed, the normal performance
expected is violated and an anomaly is discovered. The work
scheme of the normal behaviour models is represented in Fig. 5.
Three normal behaviour models are created for diagnosis
and health monitoring of the windturbine gearbox:
- Gearbox bearing temperature model;
- Gearbox thermal difference model;
- Cooling oil temperature model.
The next section describes the process followed to obtain the
normal behaviour model corresponding to the gearbox bearing
temperature. The two models related to the gearbox, and other
models developed in SIMAP for other components, follow the
same procedure to be created.
4. Normal behaviour model for the windturbine
gearbox bearing temperature
In order to develop a normal behaviour model for the
windturbine gearbox bearing temperature, a selection of
accessible on-line variables that can explain such temperature
must be done. The process of selection can be different
depending on the type of model, but normally it is based on the
physical symptoms that characterize the anomaly to be
detected. In the case of the model proposed it is necessary
to make the following considerations.
The incidental wind mechanical power over the aerogenerator is converted in electrical power plus losses:
Pwind ¼ Pgenerated þ Plosses
The wind mechanical power is proportional to the air density, to
the area covered by the tooth of the aerogenerator and to three
times the wind speed:
Pwind ¼ 12 r A v3
Fig. 5. Normal behaviour model. Work scheme.
where r is the air density, A the area covered by the aerogenerator and v is the wind speed.
According to these equations the turn speed of the
windturbine gearbox and its work stress will depend mainly
on the power generated. Also, the temperature of the main
gearbox bearing will depend on the power generated and the
temperature of the environment in the nacelle of the
windturbine.
The main gearbox bearing is cooled by oil that is circulating
continuously. The oil is cooled in a heat exchanger by air
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Table 1
Gearbox normal behaviour models
Model
Type
Inputs
Gearbox bearing
temperature model
Multilayer
perceptron
Gearbox bearing
temperature (t 1, t 2)
Generated power (t 3)
Nacelle temperature (t)
Cooler fan slow run (t 2)
Cooler fan fast run (t 2)
Gearbox thermal
difference model
Multilayer
perceptron
Gearbox thermal difference (t 1)
Generated power (t 2)
Nacelle temperature (t)
Cooler fan slow run (t 2)
Cooler fan fast run (t 2)
Cooling oil
temperature model
Multilayer
perceptron
Cooling oil temperature (t 1)
Generated power (t 2)
Nacelle temperature (t)
Cooler fan slow run (t 2)
Cooler fan fast run (t 2)
Fig. 6. Normal behaviour model for the gearbox bearing temperature of the
windturbine.
impulsed by a fan with two speeds: low and fast. Its operation
also has to be taken into account.
Once the a priori important variables for the health condition
of the windturbine gearbox have been identified, the process of
variable selection is completed by a statistical linear analysis in
order to determine the importance of the influence of these
variables in the gearbox bearing temperature. In order to do
this, an analysis of cross-correlation and impulsional response
between the explicative variables and the bearing temperature is
done [21]. The information obtained is used to know the
influence of the explicative variables on the bearing temperature and if their influence is present at the same instant or if it is
delayed.
The results of both analysis: cross-correlation and impulsional response, show the important influence of the
explicative variables and also a delay of the maximum time
of influence. Finally the model proposed for the normal
behaviour of the gearbox bearing temperature is presented in
Fig. 6.
Fig. 7 shows an example of the performance of the model
once it was fitted when it is receiving real information about its
inputs. The figure shows the real bearing temperature of the
gearbox, its estimated value from the model and the upper and
lower confidence bands for the estimation along the time
presented at a confidence level of 95%. The conclusion is that
the prediction of the evolution of the gearbox bearing
temperature corresponds well to the real evolution of this
variable for any typical working condition of the aerogenerator.
This model will make it possible to analyse if the gearbox is
working under healthy conditions or it is under some stress that
could produce a more serious failure. If the real evolution is
going outside the bands of confidence, the gearbox is suffering
some stress process that, depending on the severity and on the
length of the time interval suffered, could evolve from an
abnormal behaviour with minor consequences to a severe
anomaly or fault.
Normal behaviour models can be developed for characterization of several types of possible anomalies. These are very
important tools for a qualitative characterization of the
component health of an industrial process (in this case an
aerogenerator), prevention of possible failures and planning of
maintenance in a context of predictive maintenance. In
particular for the case of the gearbox health condition
monitoring three normal behaviour models are created and
presented in Table 1. The first one on the table was previously
presented.
5. Detection of incipient anomalies in the gearbox
windturbine of an aerogenerator
Fig. 7. Example of the normal behaviour model for the gearbox bearing
temperature working in real-time.
Once the normal behaviour models are obtained, it is
possible to use them for the detection of possible anomalies and
for refitting the maintenance planned according to the real
health of the physical components. In order to do this the input
and output variables of the normal behaviour models are taken
in real-time and a prediction of the output is obtained in realtime too. The comparison between the values of the real and
estimated output variables is used to detect possible anomalies
to be diagnosed and to be mitigated by the corresponding
maintenance action.
The case of anomaly detection in a gearbox windturbine is
analysed in the next paragraphs as an example of how this
method works. Fig. 8 shows a real evolution of the gearbox
bearing temperature using the normal behaviour model
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M.C. Garcia et al. / Computers in Industry 57 (2006) 552–568
rules as a method for knowledge representation and uncertainty
based on fuzzy sets. This is represented in Fig. 1 as Diagnosis
Expert Module. In order to demonstrate how SIMAP works in
real-time diagnosing anomalies, the example of an anomaly
from the previous section will be continued here.
The anomalies detected in the gearbox of the A3 windturbine are passed on to the Diagnosis Expert Module of
SIMAP. Its knowledge base contains information about the
following failure modes in the windturbine gearbox:
FM1: failure in the gearbox main bearing;
FM2: unbalance of the gearbox main shaft;
FM3: failure in the gearbox cooling circuit.
The knowledge base of the expert system contains the
following two rules related to these failure modes:
Fig. 8. Real and estimated gearbox bearing temperature when an anomaly is
present.
obtained in the previous section. This figure covers a time period
of 4 days (March 5–8 all inclusive). It is clear that around March
6, the real value of the bearing temperature is going outside the
upper band of the normal value predicted for this temperature.
The residual or difference between the real and estimated values
of the bearing temperature is growing from March 6 on, but the
gearbox is still working. Finally, on March 8 the gearbox fails and
the windturbine is unavailable to produce energy.
Also, Fig. 8 demonstrates the ability of a normal behaviour
to detect anomalies before a catastrophic situation is present.
Fig. 9 shows the gearbox thermal difference normal
behaviour model during the same period of time shown in
Fig. 8. Fig. 10 shows the cooling oil temperature model during
the same period of time.
It is easy to conclude from Figs. 8 and 9 that an anomaly is
present on March 6 that does not correspond to the normal
behaviour expected, however from Fig. 10 it is possible to
deduce that the anomaly does not affect the cooling circuit of
the gearbox. Therefore, the anomaly is on the side of the inner
part of the gearbox.
Once again these examples demonstrate that the normal
behaviour models obtained are very useful for two different
reasons. First, they can detect anomalies that do not correspond
to normal behaviour and that can evolve to catastrophic
failures. Second, they can be used for a qualitative estimation of
the health condition of a component based on the stress or
residual obtained from the difference between real and
estimated values. Both aspects can be used for re-planning
the predictive maintenance according to the real situation of a
component.
6. Diagnosis of anomalies related to the gearbox of an
aerogenerator
Once anomalies have been detected it is necessary to find the
causes so that SIMAP can try to diagnose them. For this
purpose SIMAP uses a fuzzy expert system based on production
According to the values that SIMAP took in real-time on
March 6 (see Section 5) and once they were fuzzified using
typical methods of fuzzyfication, the Diagnosis Expert Module
had the information about the anomalies detected and also the
following:
- Gearbox main bearing temperature is HIGH with membership
degree 1.0.
- Gearbox thermal difference is HIGH with a membership
degree 0.9.
- Cooling oil temperature is NORMAL with a membership
degree 1.0.
The expert system put all this information in its facts
database and found in its knowledge base the knowledge useful
to diagnose the situation. Here, it found the previous two rules,
but only the rule r_m1 satisfies its conditions in a degree of 0.9
M.C. Garcia et al. / Computers in Industry 57 (2006) 552–568
559
Fig. 11. Fuzzy certainty degree of diagnostics concluded.
Fig. 9. Real and estimated gearbox thermal difference when an anomaly is
present.
(minimum of the three conditions) and as a consequence, fired
its conclusions using the fuzzy inference rule specified in the
rule. This diagnosis rule concludes that the two possible failures
exist along with their associated fuzzy certainty degrees. They
are presented in Fig. 11.
The inference process issued the following diagnoses:
- Failure in the gearbox main bearing is certain in a degree
0.9752.
- Unbalance of the gearbox main shaft is quite certain in a
degree 0.75.
7. Diagnosis of anomalies related to the gearbox of an
aerogenerator
According to Fig. 1 in parallel to the Anomalies Detection
Module and also using as inputs the normal behaviour models
Fig. 10. Real and estimated cooling oil temperature when an anomaly is
present.
results, the Health Condition Assessment Module is able to
conclude the gearbox health condition on-line.
This module uses knowledge obtained from real evolutions
of anomalies that finished in failures. The similarity of these
histories of failures is compared and a pattern is obtained that
can be used as reference of the health condition of the
components. If the evolution of the life of a component is close
to the pattern of some failure mode previously developed, it is
possible to estimate the current health condition by reference to
the end point of this failure pattern. As in previous sections, the
case of the gearbox windturbine will be analysed as an example
that demonstrates how the Health Condition Assessment
Module works in real-time.
Before this module can work in SIMAP, the failure patterns
have to be developed, and for that, the history of failures in the
gearbox has to be analysed. As an example the history of six
past failures corresponding to the failure mode FM1 defined in
Section 6 are analysed. Fig. 12 shows the evolution of the
residual (distance between real and estimated values)
corresponding to the three normal behaviour models available
for the detection of the failure modes described in Table 1 in the
windturbine gearbox.
The residuals of all the histories are fuzzified and all the
fuzzy sets are aggregated using the T-conorm maximum. Thus a
common pattern can be obtained of typical residual values for
all the histories along their evolutions to the failure mode FM1.
Fig. 13 shows the fuzzy patterns resulting for the three models
of normal behaviour analysed. Fig. 13a shows three fuzzy sets
(from left to right): normal, high and very high for the gearbox
bearing temperature. Fig. 13b shows three other fuzzy sets
(from left to right): normal, high and very high for the gearbox
thermal difference. Fig. 13c shows two fuzzy sets (from left to
right): normal and high for cooling oil temperature of the
gearbox.
Once the fuzzy patterns for the failure mode FM1 have
been obtained, it is possible to estimate their sensibility for
identification of this failure mode. The fuzzy pattern
corresponding to the gearbox bearing temperature has a
sensibility of 1 (maximum of the scale), 0.8522 is the
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M.C. Garcia et al. / Computers in Industry 57 (2006) 552–568
sensibility of the fuzzy pattern corresponding to the gearbox
difference thermal and 0 is the sensibility of the fuzzy pattern
of cooling oil temperature for the failure mode FM1. Making
similar analysis for the other two failure modes of the
gearbox, it is possible to obtain a set of knowledge rules based
on the real histories of failures that will be used for the
estimation of the health condition of the gearbox. These rules
include a certainty factor that depends on the sensibility of the
fuzzy patterns to the different failure modes. For example,
from the fuzzy pattern of residuals coming from the normal
behaviour model of the gearbox bearing temperature it is
possible to deduce the following three rules about the health
condition of the gearbox:
schedules a maintenance plan which contains the actions
capable of avoiding or mitigating the failures present in the
gearbox, according to several technical and economical criteria
while maximizing windturbine availability and minimizing
maintenance costs. In order to reach this functionality, this
module performs several steps:
- Gearbox failure time forecasting. This task employs gearbox
failure histories for comparison and current time failure
forecasting.
- Preventive maintenance actions recommendations which try
to avoid or mitigate diagnosed gearbox failures.
- Maintenance tasks optimal time evaluation. This step
compares the preventive maintenance plan with the corrective
maintenance plan, both suitable to the diagnosed failures, and
concludes the best maintenance moment for preventive tasks
according to technical and economical criteria.
- Finally, maintenance tasks scheduling according to their
priority and optimal application times as well as other criteria
such as:
- Aerogenerator production plan. Maintenance scheduling
pursues to interfere the least possible with the production
plan for maximizing windturbine availability.
- Maintenance tasks costs.
- Variable maintenance resources (mainly personal, machines
and non-renewal material) as well as different relations
among maintenance actions: precedence relations, compatibility relations, etc.
All these steps will be described in the next subsections.
8.1. Predictive maintenance to be applied to the gearbox of
an aerogenerator
These rules and the other corresponding to the different
fuzzy models are used by the Health Condition Assessment
Module in real-time to estimate the health condition of the
different components. In the example developed here concerning the gearbox, its health condition is evaluated in real-time
according to the information available in SIMAP and Fig. 14
shows its evolution. It is possible to see that the health of the
gearbox passed from normal to bad condition on March 6, and
finally on March 8 passed to very bad condition. The
catastrophic failure in the gearbox happened some hours later.
8. Predictive maintenance to be applied to the gearbox
of an aerogenerator
Using both sources of information (failure diagnoses and
gearbox health condition), the Predictive Maintenance Module
The dynamic of the residuals coming from the normal
behaviour models when a failure has occurred includes
important information about the failure. It is similar to a
signature of the failure. The residuals obtained from the normal
behaviour models do not have information when a normal
behaviour is present. The residuals have a probability
distribution very close to the typical white noise. However,
when an anomaly appears and it finishes in a failure, the proper
residual has the information of the failure dynamic that does not
correspond to normal behaviour. If this happens, it is possible to
develop models for the residuals representing failures occurred.
The following real case can demonstrate how SIMAP can
predict the time remaining till failure of the windturbine
gearbox for the example analysed in the previous sections.
Fig. 7 presented the estimated and predicted values for the
gearbox main bearing temperature. Their difference is the
residual for this normal behaviour model. This evolution
finished in a catastrophic failure of the gearbox, however
suppose that this is not known and our time is set 2 days before
March 6. Would it be possible to predict the time remaining till
the failure? The response is yes.
Taking as pattern of reference the historical dynamic
evolution of residuals corresponding to failure modes in the
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Fig. 12. Evolutions for six histories of the failure mode FM1 of the residuals corresponding to the normal behaviour models of: (a) gearbox bearing temperature, (b)
gearbox thermal difference, (c) cooling oil temperature.
gearbox, it is possible to know how close the current evolution
is to the residual in an abnormal behaviour to the pattern of
historical residuals for fault situations.
Fig. 15a shows the evolution of the residuals in a historic
case that finished in a gearbox failure (higher trend) and the
current evolution of the residual for the normal behaviour
model corresponding to the gearbox main bearing temperature.
First, in order to use the historical residual pattern of failure of
this model, it is necessary to test the similarity with the current
evolution of the residual. Some pre-processing work has to be
done before comparing both evolutions of residuals. It is
necessary to make a translation of time origin to fit the
evolutions of the abnormal behaviour to similar start time. This
is obtained by trying different common starting points of
anomalies and taking the situation where the distance between
both series is minimum. Also, it is necessary to ensure that both
evolutions of residuals to be compared correspond to similar
conditions. In order to do this, a model about the dynamical of
the residuals for the historical case that finished in the gearbox
failure is created. This model represents how the failure
appears added to the normal behaviour model and characterises the failure. The model found for this case is the
following:
This model shows that the evolution of the residual when a
failure is present depends on the working conditions of the
gearbox and, also, from the previous residual of the gearbox
health. Once the model is obtained, the current conditions of the
gearbox are passed as inputs through the model. This allows for
verification if the current working conditions of the gearbox
stimulates the failure model in a similar way to how the
residuals evolve now. The similarity is observed between the
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Fig. 13. (a–c) Fuzzy patterns for the residuals of the three normal behaviour models aggregating information of six histories that finished in the failure mode
FM1.
current evolution of the residual of the normal behaviour model
for the gearbox main bearing temperature and the output of the
residual model built with historic data of gearbox failures but
excited with current conditions as inputs. Finally, it is possible
Fig. 14. Gearbox health condition during failure period.
to obtain a possibility distribution from the different distances
and this can be approximated by a triangular fuzzy set like this
presented in Fig. 15b.
Using the model of the residuals previously mentioned and
the current evolution of the gearbox life, it is possible to predict
the evolution of the residual and also its uncertainty. The
prediction is done from an instant of time till the moment where
the historical pattern of failure finished. In this case if the date
is March 6 with the anomaly present, it is possible to predict the
time remaining till failure, if the current conditions correspond
to a failure of the gearbox that can be detected by the normal
behaviour model of the main bearing temperature. This is
shown in Fig. 15c. Finally, Fig. 15d presents the fuzzy time to
failure. The centre of gravity of this triangle is 26.5 h.
This means that from now (March 6) the most important
possibility of failure will occur in 26.5 h and a probable
interval of failure between 24.5 and 28.5 h. It is easy to observe
that the prediction of failure time corresponds very well with to
reality because on March 8 this gearbox had a catastrophic
failure.
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563
Fig. 15. (a–d) Gearbox failure time forecasting.
This entire process is done for every series of residuals
coming from different models of normal behaviour. Also this
process is repeated when new information is coming to SIMAP
about anomalies and the prediction of times to failure are
updated according to the real life of the aerogenerator for each
moment. Fig. 16 shows an example of similarity between
residuals of a current and pre-processed historical evolution of
failure conditions. They fit very well.
8.2. Predictive maintenance to be applied to the gearbox of
an aerogenerator
In Section 6, the Diagnostic Expert Module of SIMAP
diagnosed two possible causes of the anomaly detected. They
were:
- Failure in the gearbox main bearing is certain in a degree
0.9752.
- Unbalance of the gearbox main shaft is quite certain in a
degree 0.75.
According to these diagnoses, the maintenance expert
system included in the Predictive Maintenance Module (Fig. 1),
selects the suitable knowledge rules to recommend maintenance actions. They can be corrective maintenance actions or
preventive maintenance actions. The following two rules were
selected from the knowledge base for this case. The numbers
are the certainty factors.
The priorities of the maintenance actions are elaborated
using the criticality of the components and the certainty of the
diagnoses issued. In this case, the maintenance actions
recommended would be:
PMA1: gearbox bearing repair, priority 0.89;
CMA1: gearbox replacement, priority 0.9;
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distributions is the best moment to execute a preventive
maintenance action according to the health of the gearbox and
the costs involved. This cross-point is the moment where the
preventive maintenance plan reaches a similar profile to the
corrective maintenance plan.
8.4. Optimal time to apply the maintenance actions to
correct the gearbox anomalies
Fig. 16. Similarity between residuals of current and pre-processed historical
evolution of failure conditions.
PMA2: shaft alignment, priority 0.89;
CMA2: shaft replacement, priority 0.9.
8.3. Optimal time to apply the maintenance actions to
correct the gearbox anomalies
According to all the previous information about the
anomalies detected in the gearbox, some maintenance
actions can be executed. Some of them are of preventive
nature and others are corrective. SIMAP evaluates in realtime if it is possible to perform preventive maintenance
actions at the current moment or if it is better to perform a
corrective plan. In order to do this, several considerations
are performed around both plans of maintenance. As an
example, next the optimum time will be estimated to perform a
preventive maintenance action for the gearbox bearing
replacement. The idea is to compare from different items
both plans: preventive and corrective and to find the crosspoint of both. This point will be the optimum moment to
perform a preventive maintenance action taking into account
the health condition of the component and the economical
impact.
The optimal time to perform a maintenance action is
calculated taking into account the risk of failure, estimated
from the prediction of the time to failure of the gearbox (see
Section 8.1), the criticality of the component to maintain, the
index of convenience for apply the maintenance action from a
technical point of view and the costs of both types of
maintenance plans: preventive and corrective. All the
processes for the case analysed are presented in Fig. 17. It
is based on a combination of the six different fuzzy sets that
represent the mentioned factors on the left side of Fig. 17. The
conclusions of these combinations are two fuzzy distributions:
one for the preventive maintenance plan and another one for the
corrective maintenance plan. They are represented at the top
right of Fig. 17. The cross-point of both possibility
The scheduling of maintenance actions is a dynamical fuzzy
maintenance scheduling task [16,22,23]. Fuzzy scheduling is
used because the maintenance cost and duration uncertainties
are represented by means of fuzzy sets. The maintenance is
dynamical because each time any of the scheduling inputs
change (production plan, maintenance actions to schedule,
maintenance resources availabilities, etc.) this module reschedules on-line a new optimal maintenance plan, according to this
new situation.
Next a real case of rescheduling is briefly described in order
to present the main features of the scheduling of maintenance
tasks.
In Section 8.3 two maintenance actions were recommended
in the windturbine AE3. They were:
PMA1: gearbox bearing repair, priority 0.89;
PMA2: shaft alignment, priority 0.89.
Both tasks, coded as MA3 and MA8 in this case, must be
done when a previous maintenance has been done MA2
(generator shutdown and gearbox opening and cleaning).
Furthermore, at the same time SIMAP recommended to
perform the following maintenance actions in other aerogenerators:
- two maintenance actions, MA0 and MA5, for fitting the
control system of the aerogeneretors AE24 and AE38;
- one maintenance action, MA7, for cleaning the hydraulic
system in the aerogenerator AE40;
- two maintenance actions, MA1 and MA6, for cleaning the
cooling systems of the aerogenerators, AE11 and AE12;
- two maintenance actions, MA4 and MA9, for testing
communications with two points from the windfarm.
The information about each maintenance action includes:
duration, priority, required conditions to be done, costs,
personnel, equipment and material required, compatibility to
be executed with other maintenance actions and previous
actions required.
In this example all this information about the ten
maintenance actions mentioned is presented in Table 2.
The information found in Table 2 plus that which is
related to the production planning and availability of the
different resources required are the input to an genetic
algorithm based on fuzzy sets. This genetic algorithm tries to
minimize time and overall costs of the maintenance actions
planned keeping the production plan and using the available
resources. The results obtained for the scheduling of
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Fig. 17. Optimal time to apply a maintenance action.
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Table 2
Maintenance action information
9. Evaluation of the maintenance effectiveness and cost
Finally, a real case is presented showing the estimation of
the maintenance effectiveness of two different maintenance
tasks:
- 24-month preventive gearbox maintenance set (MP24);
- 36-month preventive gearbox maintenance set (MP36).
maintenance actions are presented by SIMAP in a format
similar to that presented in Fig. 18.
If a rescheduling is performed when a maintenance planning
is in execution, the genetic algorithm takes into account this fact.
There are six available maintenance histories corresponding
to similar aerogenerators: two for the MP36 maintenance action
set and four for the MP24 set. Fig. 19 shows the calculated
efficiency of these maintenance tasks in respect to the measured
gearbox health condition at the moment that each maintenance
action was applied. It is possible to observe that in the case of
the MP24 maintenance action set, there is a clear inverse
relation between the gearbox health condition and maintenance
efficiency and from here the conclusion is that when the
gearbox health condition is worst (that is, the most degraded
gearbox), much lower is the efficiency of the MP24
maintenance action and vice verse. In the case of the MP36
maintenance set, no clear relation is observed and therefore no
conclusion can be determined between maintenance efficiency
Fig. 18. Maintenance scheduling.
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567
- perform a dynamical multi-objective non-linear optimization
with constraints, by means of genetic algorithms;
- represent the uncertainty inherent to the knowledge issued, by
means of fuzzy logic.
An example of how SIMAP works has been presented,
focusing on the on-line health condition monitoring of a
windturbine gearbox.
Future works are oriented to the monitoring and experience
derived from the new maintenance plan implemented in
different windfarms.
References
Fig. 19. MP36 and MP24 maintenance sets efficiency evaluation.
and the gearbox health condition. Another conclusion is that the
MP36 set is more efficient than the MP24 set, this is reasonable
because the MP36 set comprises more maintenance actions
than the MP24 set.
10. Conclusions
The main new features of SIMAP in the field of diagnosis
and maintenance introduced in this paper have been the
following:
- the integration and cooperation of every task involved in a
formal predictive maintenance strategy, that is, mainly:
continuous monitoring, incipient failure detection and
diagnosis, health condition evaluation, predictive maintenance scheduling and effectiveness measure of maintenance
actions performed;
- on-line and automatic components health condition evaluation, based on a degradation perspective;
- a maintenance scheduling method which considers both
technical and economical criteria;
- on-line, direct and automatic measurement of applied
maintenance actions effectiveness, by means of the change
observed in the health condition and degradation of the
components affected by these maintenance actions.
Furthermore, this study concludes that artificial intelligence
and modelling techniques are adequate for reaching the main
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- model dynamic non-linear industrial processes, by means of
artificial neural networks;
- characterize and represent both quantitative knowledge coming
from historical data (by means of artificial neural networks) as
well as qualitative knowledge coming from maintenance and
operation experts (by means of expert systems);
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Dr. Miguel A. Sanz Bobi is professor at the Computer Science Department and also researcher at the
Institute for Research and Technology (IIT) both
inside the Engineering School of the Pontificia
Comillas University, Madrid (Spain). He divides
his time between teaching and research in the artificial intelligence field applied to diagnosis and
maintenance of industrial processes. He has been
the main researcher in more than 35 industrial
projects over the last 20 years related to the diagnosis in real-time of industrial processes, incipient detection of anomalies
based on models, knowledge acquisition and representation, reliability and
predictive maintenance. All these projects have been based on a combination
of artificial intelligence, new information technologies and data mining
techniques.
Maria Cruz Garcı́a is PhD in industrial engineering
at University Pontificia Comillas, Spain. She worked
at National Grid Company, UK, in 1997, developing
a real-time monitoring and analysis system of the
electrical network stability and robustness. Afterwards, she worked at the Technology Investigation
Institute in the University Pontificia Comillas as a
researcher involved in artificial intelligence projects
in collaboration with Spanish utility and wind-power
generation companies (Repsol, Unión Fenosa, Molinos del Ebro). Her PhD thesis was related to the planning and effectiveness
assessment of predictive maintenance applied to industrial processes, using for
that purpose dynamic modelling techniques as well as artificial intelligence. In
2004, she joined the Spanish Savings Bank Caja Madrid, working in a research
project for developing real-time automatic trading systems for fixed income
futures, equity and forex products. These trading systems use neural networks,
quantitative and technical analysis techniques. Currently, she is a quantitative
credit analyst at BSCH. Her areas of interest are: artificial intelligence, data
mining, quantitative techniques applied to financial markets, process modelling
and signal processing techniques.
Javier del Pico-Aznar is a mechanical engineer at the University of
Zaragoza in Spain. During his years of engineering studies, he worked at
various different power plants. At present, he is the Director of the energy area
of SAMCA, a Spanish company working in different industrial sectors including energy generation. He has participated in the development of several
projects concerning the optimization of processes, cogeneration plants and
windfarms.