Power Plants Failure Reports Analysis for Predictive Maintenance

Vincenza Carchiolo

, Alessandro Longheu

, Vincenzo di Martino

and Niccolo Consoli

Dip. di Matematica e Informatica, Universita’ di Catania, Viale Andrea Doria 6, Catania, Italy

Dip. di Ingegneria Elettrica, Elettronica e Informatica, Universita’ di Catania, Viale Andrea Doria 6, Catania, Italy

BaxEnergy, Catania, Italy

Keywords:

Predictive Maintenance, Natural Language Processing, Ontologies, Wind Turbines, Renewable Energy.

Abstract:

The shifting from reactive to predictive maintenance heavily improves the assets management, especially for

complex systems with high business value. This occurs in particular in power plants, whose functioning is

a mission-critical task. In this work, an NLP-based analysis of failure reports in power plants is presented,

showing how they can be effectively used to implement a predictive maintenance aiming to reduce unplanned

downtime and repair time, thus increasing operational efﬁciency while reducing costs.

1 INTRODUCTION

It can be nowadays considered as a matter of fact

that climate changes and the increasing energy de-

mand are competing factors that push for deep ex-

ploitation of renewable energies (IEA, b); in partic-

ular their market share is expected to grow up to the

40% the next ﬁve years (IEA, a), overcoming the con-

tribution of coal and gas.

Among all sources as hydropower, geothermal,

solar and others, the forecasts for wind as renewable

energy endorse its position as one of the most rele-

vant (estimated in 2018 as 43% higher with respect

to 2015 (Council, 2019)). Wind power comes mainly

from wind turbines, that are mechatronic devices us-

ing blades and rotor to convert wind into mechanical

energy and shafts and generator to transform motion

into electrical energy.

Wind turbines (WT) industry is growing faster and

faster and larger devices are being developed, though

WTs are continuously exposed to (possibly) extreme

weather conditions, resulting in signiﬁcant mechani-

cal stress and moreover both on-shore, as well as off-

shore placement, is often characterized by restricted

accessibility. This scenario can heavily impact on

WTs reliability, often leading its components to fail

during WTs’ lifetime (Guolin et al., 2016), therefore

making the Operation and Maintenance (O&M) a crit-

ical activity that actually impacts for up to 30% of the

WT life cycle (Fischer et al., 2012).

In particular, since any technical intervention (e.g.

replacing a WT damaged part) can be very expensive

and, moreover, the lack of power generation during

downtimes also impacts on revenues (Herbert et al.,

2010), several strategies can be adopted to limit these

expenses (Abichou et al., 2014), from condition mon-

itoring (CM) systems that falls into the so-called sig-

nal processing approach (signals can be WT blades

vibration, or acoustic emissions and/or thermography

measurements of WT internal components), to nu-

merical models of WTs, or data-driven strategies e.g.

based on SCADA (supervisory control and data ac-

quisition) (M

arquez et al., 2012) (Nabati and Thoben,

2017).

All these strategies can be effectively exploited to

endorse the Predictive Maintenance, which focuses

on identifying the optimal time to perform mainte-

nance, in particular after some WT working condi-

tion starts to decline and performance to decrease,

but before failure occurs. It, therefore, aims to es-

tablish a trade-off between preventative maintenance,

which uses strict time-based scheduling and may oc-

cur (and cost) too frequently, and reactive or run-to-

failure maintenance, where components are repaired

only after they have already failed (Selcuk, 2017).

Current studies on WT focused on predictive

maintenance because good wind turbine reliability,

together with predictable wind turbine maintenance

schedules, will result in reduced cost of energy

and then wind farm project success. This is even

more important for offshore wind farms because of

their higher initial capital cost and limited acces-

sibility causing higher operational and maintenance

costs (Qiu et al., 2012).

A traditional approach to predictive maintenance

is based on the use of SCADA systems, that provide

rich information about the plant itself giving signal

information and component information.

404

Carchiolo, V., Longheu, A., di Martino, V. and Consoli, N.

Power Plants Failure Reports Analysis for Predictive Maintenance.

DOI: 10.5220/0008388204040410

In Proceedings of the 15th International Conference on Web Information Systems and Technologies (WEBIST 2019), pages 404-410

ISBN: 978-989-758-386-5

There are a lot of research that, focusing on these

systems as a primary source of entry, has achieved

a good success in reporting failures and problems

within the plant through power-curve and temperature

analysis (Nabati and Thoben, 2017).

Some of these research outcomes have been rec-

ognized by industry and turned into applications. The

main advance in this approach is declined in the use

of artiﬁcial intelligence to analyze the collected infor-

mation. For example (Huuhtanen and Jung, 2018) use

convolutional neural networks (CNN) for monitoring

the operation of photovoltaic panels. The predictive

maintenance in this approach is activated when it is

observed a large deviation between predicted and ac-

tual (observed) power curve, whereas (Helbing and

Ritter, 2018), (de Azevedo et al., 2016) and (Romero

et al., 2018) show example of the analysis of SCADA

data through the use of deep learning for fault detec-

tion in wind turbines.

Improving wind turbine reliability requires to re-

duce downtime and increase availability by optimiz-

ing its design and imposing a well-organized main-

tenance schedule. This requires a full understand-

ing of the system and a detailed analysis of its fail-

ure mechanisms and cases, therefore in addition to

SCADA systems, a good approach is the exploita-

tion of maintenance report, whose content can be

effectively analyzed to extract relevant information

about WT failure components, endorsing the predic-

tive maintenance approach.

A maintenance report is a document in which

there are important data about the WT and in which

there probably is the main cause that led to the cor-

respondent status of the plant at that time. Our goal

is to ﬁnd a correlation between the data shown in the

maintenance reports and the possible causes of fail-

ure; to this purpose, we use Natural Language Pro-

cessing (NLP).

NLP was formerly known as Computational Lin-

guistics in the 60s (Wagner, 2016) and it uses compu-

tational techniques and artiﬁcial intelligence to under-

stand, learn and synthesize human language content.

The foundations of NLP lie in several disciplines as

computer and information sciences, linguistics, arti-

ﬁcial intelligence and robotics, psychology, philoso-

phy, logic and mathematics, electrical and electronic

engineering. Applications of NLP include machine

translation, natural language text processing and sum-

marization, user interfaces, multilingual and cross-

language information retrieval, speech recognition,

artiﬁcial intelligence, and expert systems, and many

others. Recently, in particular in the last 20 years, this

affected not only scientiﬁc research but also practi-

cal applications that have been successfully integrated

into consumer products as the Apple Siri (Apple, ) or

Microsoft Cortana (Microsoft, ) personal assistant, or

even in more speciﬁc context, ranging from personal

medical records data gathering, feeding and analy-

sis (Carchiolo et al., 2015) (Carchiolo et al., 2015)

to Twitter and/or Web data discovery for several pur-

poses (Carchiolo et al., 2015) (Longheu et al., 2016).

Here we adopt a NLP-based approach together

with Ontology-based information extraction for cap-

turing syntactic and semantic relations within words,

allowing to leverage maintenance phrases discovering

what went wrong and determined the failure.

The rest of paper is organized as follows: In sec-

tion 2 we address the question of analyzing mainte-

nance report, whereas in section 3 the overall project

that leverage information coming from reports is dis-

cussed; section 4 brieﬂy concludes our work and show

future directions.

2 MAINTENANCE REPORT

NLP-BASED ANALYSIS

This section presents an analysis and methodology

applicable to the data that have been extracted from

maintenance reports of WTs.

In pursuing this goal, the ﬁrst problem is to dis-

cover the meaningful information hidden in mainte-

nance reports and to collect all useful data from.

The main problem is to discover all the informa-

tion hidden in the maze of a maintenance report that

it can, by its nature, be very varied in form and infor-

mation. The information presents in a maintenance

report can have different view:

• structured versus unstructured information

• fault reporting information vs. repairing operation

• measurable and non-measurable information

For example, the fault reporting can be described

by either a well-coded data (almost structured) as the

alarm code provided by a software tool or a qualitative

description, via a natural language, of the unexpected

behavior.

In the same way, the repairing operation descrip-

tion can contain the list of the WT replaced parts (each

with numbers and costs), the operations carried out

for the repair, the work hours but also information

about the success of the technical intervention; the lat-

ter, probably, hidden in a descriptive sentence written

in some natural language.

Fig 1 shows some examples of wind turbine main-

tenance report and it is notable how they contain a

lot of information, with different spatial organization,

Power Plants Failure Reports Analysis for Predictive Maintenance

405

Figure 1: Maintenance Report Examples.

different degree of structuring (free test, lines, num-

ber, error code, and so on), several forms and some-

times using also different languages. In fact, each ev-

ery wind turbine manufacturer use a customized form.

It is clear that such data require different techniques

due to their heterogeneity, e.g. data mining, sentiment

analysis or similar.

Limited research activities have been conducted

for detecting failures related to WTs. Sometimes,

NLP techniques are applied for the identiﬁcation of

technology trends (Lee and Lee, 2013); they are

though applied with the aid of large available input

repositories. Those inputs consist of a lot of patent

documents from which the analysis can start. The

work uses those techniques to identify new innova-

tion patterns in the ﬁeld of energy technologies.

There are other similar works that focus on a large

dataset of unstructured data and apply those tech-

niques for knowledge discovery of accident informa-

tion in the text. The involved text mining pipeline fo-

cuses on those inputs to recognize the main risk fac-

tors, which can be similar to the case of WTs main-

tenance report discussed in this paper. Those acci-

dent dataset has been collected over a 12-month pe-

riod, through collecting data from different available

accident datasets on the network. A smaller portion

of those documents has been recognized by an expert

analysis team and has been used to build the ontology

terms in the model proposed in (Ertek et al., 2017);

however, this study cannot be used in our use case

since there are not equivalent failure dataset available

in the network.

There are other researches on the text mining

domain that focus on a different technique named

FMECA (Failure Mode, Effects and Criticality Anal-

ysis), that is recognized as one of the earliest tech-

niques for failure analysis (Bouti and Kadi, 1994).

FMECA is used to analyze the reliability and safety

of equipment with inductive logic. This work is pretty

similar to our use case since it will involve the de-

velopment of an ontology-based model with detailed

concepts. Based on the analysis of the main re-

search contents of fault diagnosis coming from ma-

chine tools there are four main concepts that has been

recognized by this work. They are known as fault phe-

nomenon, fault maintenance, fault cause and fault lo-

cation and they are seen as fault events (Zhou et al.,

2017). Those concepts will be used to retrieve text

information inside the text with the use of clustering

techniques. However, this approach cannot be use-

ful to our domain case because it reﬂects a superset

of our failure maintenance reports sentences and be-

cause the developed ontology has been build with a

bigger dataset and with speciﬁc domain information

WEBIST 2019 - 15th International Conference on Web Information Systems and Technologies

406

like frequencies of failures, severities, locations, mo-

tivations that are not available in our use case.

In (K

uc¸

uk and Arslan, 2014) authors propose a

semi-automatic approach to build an ontology model

for wind energy domain. This process involve the

crafting process of different Wikipedia articles related

to the wind energy domain. However as the previous

one this cannot be used to setup the proposed ontol-

ogy model because it reﬂects a different domain case.

Our solution involves NLP techniques and

Ontology-based information extraction for capturing

syntactic and semantic relations within words in a

text, as speciﬁed previously. Data is composed of

maintenance reports regarding WTs maintenance ac-

tivities and provided by Bax Energy. A main short-

coming is that reports provided as input are not

enough, because only a small part of them contains

analyzable data.

These reports make references to maintenance

phrases that can be analyzed to ﬁnd out what went

wrong or not within the same report. The use of

these techniques will allow identifying domain con-

cepts within the text and if they respect the relation-

ships of the proposed ontology. In fact, it contains all

the relationships that have been built between a single

failure and potential agents of failure that can provoke

it, structural components of the plant that may incur

at that speciﬁc failure and the condition that triggered

that failure event for that speciﬁc component. So, the

work shows the combination of a semantic module,

that is based on the ontology information retrieval,

with a set of natural language processing techniques

that goes deep into the sentence to clean up it, tok-

enizes it and retrieves possible syntactic dependencies

that are useful to build up domain individuals.

3 THE WEAMS PROJECT

In the context of WTs, if we want to ﬁnd a correla-

tion between the data shown in the maintenance re-

ports and the possible causes of failure, ﬁrst of all,

it is necessary to gather a signiﬁcant data amount for

an analysis purpose. This data can already be col-

lected from the developed software and also from dif-

ferent sources. In fact, the document will show how

the grafting of different types of data, or rather the ex-

tension of our dataset can not only specify and further

enrich our analysis purpose but also improve the ﬁnal

result in terms of accuracy.

In this section, we will present the solution of

Weams project; the proposal is schematized in ﬁg. 2,

where several modules are present, i.e. the Data col-

lector, data parser, data reader and matcher and ﬁ-

nally the data writer joint with translate module. All

these components are described in the following para-

graphs.

3.1 Data Collector

The ﬁrst module is named data collector and is re-

sponsible for the acquisition of the relevant parts of

interest from maintenance reports. Uploaded docu-

ments pass through the PDFBox library (PDFbox, ),

used for preliminary reading of each string in the ﬁle.

The algorithm starts by collecting all relevant

parts as deﬁned in the user template. They are di-

vided into static and dynamic parts, whose length may

vary within each user document. Since the extraction

area for ﬁxed parts is ﬁxed for every document, such

parts can be extracted immediately, whereas dynamic

parts require the algorithm looks for all the content

that belongs to them. Read strings are stored into a

speciﬁc data structure for further processing and as-

sociated with the relevant part of interest. In addi-

tion, the library provides important information like

the reference coordinates within a 2D space of each

character extracted from the document, to be used

later to remap the content. Data acquisition also pro-

vides the removal of unwanted data as speciﬁed by

the user (e.g. header/footer).

Once the noise data has been removed, the acqui-

sition operation starts by comparing each read string

with the one entered by the user in order to map that

speciﬁc part. If this comparison succeeds, the algo-

rithm will register each subsequent string in a struc-

ture that will be identiﬁed by the compared string.

The structures can have different composition, de-

pending on the nature of the current part. If it is re-

ported from the template as table, the related structure

will also have a list of TextPagination objects. Each

object has a list of TextPosition properties, the whole

information that are taken from the PDFBox library,

and the relative page in which this string has been

found. This information is important for the next step

in which the content is ordered before building the re-

lated table structure.

When the part is reported as free text, the struc-

ture stores the starting point as X-Y 2D coordinates of

each string, the related endpoint and the page where

it has been found. Since this occurs for each page, the

content of a page does not depend on others. More-

over, we consider the membership page of each string

and its order and we compare with others’ in the list

to ﬁnd the maximum value of that page. Such value

is used as the start value of the ﬁrst string on the next

page. All strings that belong to pages after the ﬁrst

will see the value of the ordinate changed, which is

Power Plants Failure Reports Analysis for Predictive Maintenance

407

Figure 2: Weams Report Architecture.

not related to the previous page. This value is modi-

ﬁed with the sum of the maximum value found on the

previous page, i.e. the maximum ordinate (Y) of the

last useful string on the previous page, and their cur-

rent position on that page. This procedure for eval-

uating minimum and maximum values for each page

is performed for all the pages in the document. Af-

ter establishing a vertical data order, we then aggre-

gate each row’s content by a horizontal arrangement.

Once these operations are completed, the content thus

ordered and collected for each part of interest is ready

to be further processed by the parser.

3.2 Data Parser

This module takes as inputs the different parts ex-

tracted from Data Collector and processes the parts

in tabular format. In fact, the tabular information is

still not homogeneous therefore it will be necessary to

create a structure in rows and columns for each record

mapped within the part being examined. The process

of building the table foresees the use of the TrapRange

library (Luong, 2015) which has been modiﬁed for

our case. Speciﬁcally, the changes made allow a cor-

rect interpretation of tables whose headings are di-

vided into several lines, or in which there are columns

that subsequently branch into two or more columns

and a correct interpretation of the rows of a table that

can develop on more lines.

Concerning the ﬁrst point, the user can specify in-

side the template which columns exhibit bifurcations,

passing the index of the column of the interested table

(0-based). This will allow the algorithm to exclude

the word mapped to this index and to take into con-

sideration the columns underlying it. As a result, the

table will be in a structured and homogeneous form

and the analysis of the number of rows and columns

of the same can be carried out. This analysis is done

by the software using TrapRange which identiﬁes the

limits of each row and each column of the table.

A drawback of this point concerns the identiﬁca-

tion of multi-lines rows, that are composed of sev-

eral lines, that are not correctly recognized in the ta-

ble. Once they are processed by the software they

will have more lines than actually (the content is split

unevenly into different lines). To make sure that the

entire content is collected within a single row, a ref-

erence parameter has been chosen. It must be a value

that exists within each table of interest and for this

reason, it has been chosen as the content of the ﬁrst

column which represents always an identifying value,

always reported, or a row index, always reported too.

For collecting these reference points the algorithm

create speciﬁc intervals for each word/character; these

intervals record the height of the character and repre-

sent the range of integers that start from a lower min-

imum, the ordinate of the character, up to a higher

maximum, the sum of the Y coordinate of the charac-

ter plus the height of the character (in order to map the

interval in which a character is positioned within the

text). Intervals falling under the ﬁrst column (that has

its own interval range) constitute the content of the

ﬁrst column for each different row. In this way, ex-

cluding the null values, we have the positional ranges

for each content of the ﬁrst column for each row and

the algorithm can proceed to the second step.

WEBIST 2019 - 15th International Conference on Web Information Systems and Technologies

408

In summary, the algorithm computes an interval

for each content that falls in the columns following the

ﬁrst column, with the same indexes already described,

and the distance between the aforementioned range

and each reference interval. The minimum distance

value represents at which reference range that con-

tent refers. Once this procedure has been carried out

for each reference interval a single line can be con-

structed. It is made up of as many cells as the num-

ber of columns in the table and each cell has its own

content, even if distributed over several lines. Having

done this for each part of interest the content is con-

sidered well structured and can be further processed

to be written into the database.

3.3 Data Reader and Matcher

This module is responsible for the operation of read-

ing the instances already present in the ﬁnal system.

The model ﬁle that has been created by the system on

user entities (see the section below) comes in help for

this purpose. It contains all the properties and rela-

tions and all data types of these properties. Moreover,

it contains the address of each entity that has to be

read from the system and written into the system; it is

necessary to this module and to the writer module. In

addition, in accordance with the parameters that have

been set by the user during the creation of the tem-

plate, some of these data must already be present in

the system. As a result they must be referenced and

not created and conversely, those that are not present

in the database will be created. For the ﬁrst case, the

software reads the related objects already present in

the system for each entity referenced by the user. For

those entities that must match the software takes and

stores the relative object that match. It will be sent

as input to the writing module. Finally, the match-

ing approach is based on the user-entered properties,

different for each reference entity.

Note that for those objects that provide a match

and consequently must already exist within the ﬁnal

system, the software will stop the processing of the

current ﬁle giving an error in the case the entity is not

matched/found. It will continue with the next ﬁle in-

serted by the user in the extraction process, restarting

from the ﬁrst module (the data collector).

3.4 Data Writer and Translate Modules

The writer module stores data present in the document

on the ﬁnal system. It receives as input all the refer-

ences to instances already present in the system to be

referenced (via an identiﬁcation number) and the var-

ious patterns that will be used to create these objects

(template patterns) before writing them. These pat-

terns have been created to allow the user to extract dif-

ferent parts within a single block of text through regu-

lar expressions. The user must specify the name of the

property the related content will refer to. Moreover,

these patterns allow describing the properties of each

column within a table. Then, the rich texts that will

be involved in the analysis are translated through an-

other module. It interfaces with a service exposed by

Yandex dictionary (Yandex, ) to detect text language

and translate it into English (maintenance report in-

clude information in Deutch and other languages). It

has been developed to facilitate the construction of a

common reference dictionary. Finally, structured data

is converted into JSON format and sent to the ﬁnal

system.

4 CONCLUSIONS

The approach discussed so far shows how textual data

can be crafted and analyzed, starting from mainte-

nance reports, with the application of a set of natural

language processing and ontological techniques. The

obtained results have been satisfactory, regarding the

failure classiﬁcation, notwithstanding the limited test

dataset. This can be the ﬁrst step on a true evaluation

step that will be based on a much larger dataset than

this. Its extension will also involve a larger ontologi-

cal model that will be useful to encapsulate other fail-

ure phenomena or other more structural components

within the plant or other components which will fur-

ther enrich the text analysis. A deeper comparison

with other works in the same domain (e.g. (Kusiak

and Li, 2011)) is also planned.

ACKNOWLEDGMENTS

This work has been partially supported by Wind En-

ergy Asset Management System (WEAMS) Project,

endorsed by UE, Italian Ministry of Economic Devel-

opment (MISE) and PON ”Imprese e Competitivita’ -

Iniziativa PMI 2014-20”

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