simap: intelligent system for predictive maintenance: application to the health condition monitoring...

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 Investigacion Tecnologica, IIT, Santa Cruz de Marcenado 26, 28015 Madrid, Spainb 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

www.elsevier.com/locate/compind

Computers in Industry 57 (2006) 552–568

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 recom-

mended 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: [email protected] (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

mailto:[email protected]

http://dx.doi.org/10.1016/j.compind.2006.02.011

M.C. Garcia et al. / Computers in Industry 57 (2006) 552–568 553

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:

� C
ontinuous collection of data coming from different sensors
installed in the aerogenerator and meteorological towers.

� C
ontinuous 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.

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 aeroge-

nerator 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 (prece-

dence relations, compatibility relations, etc.).

Effectiveness of the maintenance actions applied accord-

ing 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:

- N
ormal 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].

- A
nomalies 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].

- H
ealth 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].

- D
iagnosis 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

M.C. Garcia et al. / Computers in Industry 57 (2006) 552–568554

Fig. 1. SIMAP architecture.

the windturbine component relations between symptoms

(anomalies and degradations) and failure modes.

- P
redictive 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 optimiza-

tions with variable constraints. Fuzzy logic is employed in

order to adequately represent maintenance tasks costs and

duration uncertainties [19,20].

- M

Fig. 2. Maintenance task efficiency calculation.

aintenance 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 � T

2Tmax

ðtanh ðai2Þ � tanh ðai1ÞÞ

According to Fig. 1, every new set of real measurements taken

by the data acquisition system of an aerogenerator, is passed

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 pr-

edicted and observed new sets of values are passed in parallel

through the anomalies detection module and the health con-


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 pre-

dictive maintenance scheduling module will re-plan the mai-

ntenance 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:

� M
aintenance 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.

� I
t is more cost effective, and provides the most availability,
reliability and security effectiveness.

� O
ptimization of aerogenerators life cycle by applying a
maintenance strategy that pretends to delay or reduce

components degradation.

� A
ctual 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 degrada-

tions 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.

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 wind-

turbine 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 e-

maintenance 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


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 on-

line 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:

- G
earbox bearing temperature model;
- G
earbox thermal difference model;
- C
ooling oil temperature model.
Fig. 5. Normal behaviour model. Work scheme.

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 aeroge-

nerator 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

where r is the air density, A the area covered by the aero-

generator 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


Fig. 6. Normal behaviour model for the gearbox bearing temperature of the

windturbine.

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)





Cooling oil

temperature model

Multilayer

perceptron

Cooling oil temperature (t � 1)





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 tempera-

ture and if their influence is present at the same instant or if it is

delayed.

The results of both analysis: cross-correlation and impul-

sional 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

Fig. 7. Example of the normal behaviour model for the gearbox bearing

temperature working in real-time.

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 character-

ization 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

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 real-

time 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


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 anaerogenerator

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

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 wind-

turbine 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:

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:

- G
earbox main bearing temperature is HIGH with membership
degree 1.0.

- G
earbox thermal difference is HIGH with a membership
degree 0.9.

- C
ooling 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


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:

- F

F

p

ailure in the gearbox main bearing is certain in a degree

0.9752.

- U
nbalance 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

ig. 10. Real and estimated cooling oil temperature when an anomaly is

resent.

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


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:

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 concern-

ing 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

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:

- G
earbox failure time forecasting. This task employs gearbox
failure histories for comparison and current time failure

forecasting.

- P
reventive maintenance actions recommendations which try
to avoid or mitigate diagnosed gearbox failures.

- M
aintenance 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.

- F
inally, 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, compat-

ibility 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

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


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 charac-

terises 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


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.


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:

- F
ailure in the gearbox main bearing is certain in a degree
0.9752.

- U
nbalance 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 main-

tenance 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;


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 real-

time 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 cross-

point 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

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

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 resche-

dules 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 aero-

generators:

- t
wo maintenance actions, MA0 and MA5, for fitting the
control system of the aerogeneretors AE24 and AE38;

- o
ne maintenance action, MA7, for cleaning the hydraulic
system in the aerogenerator AE40;

- t
wo maintenance actions, MA1 and MA6, for cleaning the
cooling systems of the aerogenerators, AE11 and AE12;

- t
wo 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


Fig

.17.

Opti

mal

tim

eto

apply

am

ainte

nan

ceac

tion.


Table 2

Maintenance action information

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.

Fig. 18. Maintenan

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:

- 2

ce

4-month preventive gearbox maintenance set (MP24);

- 3
6-month preventive gearbox maintenance set (MP36).
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

scheduling.


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:

- th
e 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 mainte-

nance scheduling and effectiveness measure of maintenance

actions performed;

- o
n-line and automatic components health condition evalua-
tion, based on a degradation perspective;

- a
maintenance scheduling method which considers both
technical and economical criteria;

- o
n-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

goals of this predictive maintenance strategy, due mainly to

their ability to:

- m
odel dynamic non-linear industrial processes, by means of
artificial neural networks;

- c
haracterize 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);

- p
erform a dynamical multi-objective non-linear optimization
with constraints, by means of genetic algorithms;

- r
epresent 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

[1] D.N.V. Riso, Guidelines for Design of Wind Turbines, 2001.

[2] Danish Wind Turbine Manufacturers Association, Operation and

maintenance costs for wind turbines, http://www.windpower.dk,

2000.

[3] A. Davies, Handbook of Condition Monitoring, Chapman & Hall, 1998.

[4] S. Haykin, Neural Networks: A Comprehensive Foundation, IEEE Press,

1994.

[5] A. Munoz, M.A. Sanz-Bobi, An incipient fault detection system based on

the probabilistic radial basis function network. Application to the diag-

nosis of the condenser of a coal power plant, Neurocomputing 23 (1998)

177–194.

[6] R. Iserman, Process fault detection based on modelling and estimation

methods, Automatica 20 (1984) 387–404.

[7] R.J. Patton, P.M. Frank, R.N. Clark, Issues of Fault Diagnosis for Dynamic

Systems, Springer-Verlag, 2000.

[8] M.A. Sanz-Bobi, J. Gilmartın, Condition monitoring of a hydraulic pump

using a self-organising map, in: Proceedings of the Scientific Conference

Artificial Intelligence in Industry, High Tatras, Slovakia, (1998), pp. 267–

274.

[9] M.A. Sanz-Bobi, M.A. Donaire Toribio, Diagnosis of electrical motors

using artificial neural networks, in: IEEE International Symposium on

Diagnostics for Electrical Machines, Power Electronics and Drives

(SDEMPED), Gijon, Spain, (1999), pp. 369–374.

[10] M.A. Sanz-Bobi, R. Palacios, A. Munoz, R. Garcıa-Escudero,

M. Perez, A.L. Matesanz, ISPMAT: Intelligent System for Predictive

Maintenance Applied to Trains, EUROMAINTENANCE, Helsinki,

Finland, 2002 .

[11] H. Kumamoto, E. Henley, Probabilistic Risk Assessment and Manage-

ment for Engineers and Scientists, Institute of Electrical and Electronics

Engineers Press, 1996.

[12] J. Rasmusen, Diagnostic reasoning in action, IEEE Transactions on

Systems Man and Cybernetics 23–24 (1993) 981–992.

[13] L. Rademakers, H. Braam, M.B. Zaaijer, G. van Bussel, Assessment and

optimisation of operation and maintenance of offshore wind turbines.

ECN Report: ECN-RX-03-044, June 2003.

[14] R. Yager, L. Zadeh, An Introduction to Fuzzy Logic Applications in

Intelligent Systems, Kluwer Academic Publishers, 1992.

[15] J. Villar, M.A. Sanz-Bobi, Semantic analysis of fuzzy models, application

to Yager models, in: Nikos Mostorakis (Ed.), Advances in Fuzzy Systems

and Evolutionary Computation, World Scientific and Engineering Society

Press, 2001, pp. 82–87.

[16] M.C. Garcıa, M.A. Sanz-Bobi, Dynamic Scheduling of Industrial Main-

tenance Using Genetic Algorithms, EUROMAINTENANCE, Helsinki,

Finland, 2002.

[17] D.E. Godlberg, Genetics Algorithms in Search, Optimization & Machine

Learning, Addison-Wesley, 1989.

[18] D.A. Van Veldhuizen, G.B. Lamont, Multi-Objective Evolutionary Algo-

rithms: Analyzing the State-of-the-Art. Evolutionary Computation, MIT

Press Journals 8 (2) (2000).

[19] S. Martorell, A. Munoz, V. Serradell, Age-dependent models for evaluat-

ing risks and costs of surveillance and maintenance of components, IEEE

Transactions on Reliability 45–3 (1996) 433–442.

http://www.windpower.dk/


[20] A. Chande, R. Tokekar, Expert-based maintenance: a study of its effec-

tiveness, IEEE Transactions on Reliability 47 (1998) 53–58.

[21] G.P. Box, G.M. Jenkins, Time series analysis, in: Forecasting and Control,

Holden Day Inc., 1976.

[22] K. Deb, Multi-Objective Genetic Algorithms: Problem Difficulties and

Construction of Test Problems. Evolutionary Computation, MIT Press

Journals 7 (3) (1999).

[23] M. Jahangirian, Intelligent dynamic scheduling system: the application

of genetic algorithms, Integrated Manufacturing Systems (2000)

11–14.

[24] G. Munson, The Maintenance Challenge in Wind Power, North American

WINDPOWER, Dec 2004.

Dr. Miguel A. Sanz Bobi is professor at the Com-

puter 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 arti-

ficial 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 diag-

nosis 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 Garcıa 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. After-

wards, 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, Union Fenosa, Moli-

nos 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 includ-

ing energy generation. He has participated in the development of several

projects concerning the optimization of processes, cogeneration plants and

windfarms.

simap: intelligent system for predictive maintenance: application to the health condition monitoring...

Documents