Development of an Ontology for Aerospace Engine Components
Degradation in Service
C. Okoh
, R. Roy
, J. Mehnen
, L. Redding
and A. Harrison
EPSRC Centre for Innovative Manufacturing in Through-Life Engineering Services
Department of Manufacturing and Materials, Cranfield University, MK43 0AL, Cranfield, U.K.
Rolls Royce, Derby, U.K.
Keywords: Ontology, Taxonomy, Degradation Mechanisms, Aerospace Engine, Component Degradation, Information
Extraction, Knowledge Management, Service Knowledge, Through-life Engineering Service.
Abstract: This paper presents the development of an ontology for component service degradation. In this paper,
degradation mechanisms in gas turbine metallic components are used for a case study to explain how a
taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative
process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into
classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use
in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a
robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse
processes relative to various failure events. The initial evaluation reveals improvement in the performance
of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The
information extracted and collected is required to improve service knowledge and performance feedback
which are important for service engineers. Existing research areas such as natural language processing,
knowledge management, and information extraction were also examined.
In some specific domains events recorded for
knowledge capture, sharing and reuse are usually
represented in text formats. Information extraction
(Wang et al., 2006) is employed to data when
seeking to identify and capture the required
degradation mechanisms for service knowledge
(Doultsinou et al., 2009). The concept of
information extraction is essential in the respective
domains of health care, energy, power, and
aerospace where various events are encountered in
the maintenance of machines.
An understanding of knowledge management
(KM) (Dadzie et al., 2009; del-Rey-Chamorro et al.,
2003), natural language processing (NLP)
techniques (Dale et al., 2000), information extraction
(IE), taxonomy (Saleem and Bellahsene, 2008),
degradation mechanisms (DM) (Okoh et al., 2014)
and an ontology (Ahmad and Colomb, 2007; Serra et
al., 2013) is needed to improve validated results for
better decision making.
The domain corpus is a repository of
unstructured and semi-structured information. The
task to identify, extract and retrieve the relevant data
lies in the domain of natural language processing.
The extraction of specific information from natural
language is compared with the expected data.
Information extraction is part of NLP with the task
of extracting entities such as names of persons,
locations, and organisations. In this case, Named
Entities (NE), Cause and Effect causality ordering
approaches are implemented by using the verb cue
phrase (Kim et al., 2009). The data are then
structured in the ontology.
A taxonomy is a structured arrangement of terms
and concepts (Ryu and Choi, 2006). This presents a
representation of knowledge with domain specific
concepts. In populating the ontology with terms,
duplicate words are avoided to eliminate
In this paper, the case study focuses on
evaluating the validity of the taxonomy of the
degradation mechanisms for an existing ontology.
Sanity checks were used to manually observe and
count the number of identified and captured
Okoh C., Roy R., Mehnen J., Redding L. and Harrison A..
Development of an Ontology for Aerospace Engine Components Degradation in Service.
DOI: 10.5220/0005090201080119
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2014), pages 108-119
ISBN: 978-989-758-049-9
2014 SCITEPRESS (Science and Technology Publications, Lda.)
mechanisms. The degradation mechanisms and
keywords within issues reported by service
engineers describe defects observed during
maintenance, repair and overhaul from a through-life
engineering services perspective (Roy et al., 2013).
This understanding is required to establish the
synonyms of the words to be extracted from the
corpus (Ryu and Choi, 2006). This work is based on
a case study carried out within the aerospace
maintenance domain. The contribution is the
practical use of the ‘AS-IS’ and the ‘TO-BE
framework to validate and develop an ontology
within a service maintenance domain. Sanity checks
ascertain the effectiveness of the extractor and show
improvement in the performance of the ‘TO-BE
The remainder of this paper is organised as
follows. The background of related fields is
discussed in Section 2. The research methodology is
described in Section 3. Section 4 presents and
discusses the evaluation of the results and Section 5
presents the research conclusions with regard to the
benefits of the validation and identification process.
2.1 Knowledge Management
Knowledge Management (KM) is increasingly being
implemented in global engineering and service
organisations. Knowledge acquisition, storage,
retrieval and interaction are part of KM (Dadzie et
al., 2009). It ensures information is secure and well
managed (del-Rey-Chamorro et al., 2003) with the
purpose of information reuse and sharing. KM is an
information system strategy based on insights and
experiences of domain experts to gain competitive
advantage. KM can be used to develop taxonomies
in order to produce and manage ontologies.
2.2 Ontology
An Ontology is an explicit specification of
conceptualisation (Gruber, 1993). Recent work has
shown the importance of ontology as a problem
solving tool of conceptualisation of entities
(Maedche and Volz, 2001). The concepts and
relations are used to reason and describe domain
knowledge. This is a hierarchical arrangement which
represents a more natural means of information
management in a unique domain.
Ontology assists in developing models of a
domain based on reality, perception, conceptual-
lisation, communication and interpretation (see
Figure 1). Axioms (reasoning about the meaning)
are described by means of asymmetric and
intransitive can be related to symmetric, irreflexive
and intransitive. It is aimed at capturing specific
intended and excluding non-required concepts by
reason of conceptualisation (Guarino et al., 2009). It
defines terms and relationships inside the domain.
Types of ontology include domain, representational,
application and generic.
Figure 1: The relationships between perception,
conceptualisation and language for communication
(Adapted from Source: Guarino et al., 2009).
A primary focus of most ontologies is the
taxonomy of classes and subclasses (also called
concepts and synonyms) related to different
properties (roles) (see Figure 2) which describes the
attributes with the role’s restrictions defined
(Uschold and Gruninger, 2004).
A development process to identify and extract
relevant terms or keywords is proposed in this paper.
These terms are considered as taxonomy. Taxonomy
represents knowledge acquisition of similar words.
The taxonomy extracted from a service maintenance
context for different degradation mechanisms
include Fracture with synonyms as crack, tear and,
break. An ontology is a knowledge repository of the
taxonomy with inter-relationship of a
conceptualisation of terms as illustrated in Figure 2.
Both taxonomy and ontology are sometimes used
interchangeably. However, taxonomy is often used
in industry and ontology is commonly used by
Figure 2 is an extension of the ontology tree
showing a simplistic knowledge representation with
levels relating to the mechanical component. It also
Figure 2: Example of an Ontology (Adapted from Source:
(Uschold and Gruninger, 2004).
links the component to the feature and mechanisms
with examples of defects which can affect an asset.
Identification of image and shape feature bird
classification is based on real-world objects and
semantic-based retrieval (Liu et al., 2007). There are
critical unchanging properties such as physical bird
shapes and characteristics. These are used to manage
and model bird classification for knowledge reuse.
The ontology provides a similar shared
understanding of a specific domain in both humans
and computers. It provides a semantic starting point
for meaningful definitions. The UNAS, (2000)
describes criteria for the design of ontologies
relating to the common approaches and visualisation
used in ontological engineering. Ahmad and
Colomb, (2007) argue that a server development
should be determined by considering what the
ontology is designed for (e.g. ontologies for business
and engineering applications should vary slightly).
Jasper and Uschold, (1999) present a framework for
understanding and classifying the application of
ontologies. Scenarios have been categorised into (1)
neutral authoring of a single language, (2) common
access to information by more than one person or
computer applications and (3) indexing – ontology
as a tool for indexing information.
The applicability of ontology structure is the
conceptualisation of lexical entries. It represents
concepts, the hierarchy and lexical signs for
relations and non-taxonomic relations (Maedche and
Volz, 2001).
In ontology, maintenance is related to the
pruning of the information (Sabou et al., 2005).
Unwanted keywords are eliminated in a given
domain, whilst refinement, the recognition of the
relevant keywords which are not resident in the
ontology are removed (Maedche and Volz, 2001). In
pruning, the domain relevance is determined by the
comparison of the concepts retrieved from a specific
domain with the rate of occurrence acquired from a
general domain. In refinement, the learning phase
enhances the functional capability of the algorithm,
so that, unrecognised words can be identified as
concepts and conceptual behaviours.
2.3 Information Extraction
Information extraction is one of the important sub-
areas of NLP. Named Entities Recognition (NER)
recognises named entities from within a phrase or
clause or group of sentences. The named entities can
be classified based on pre-defined terms such as
organisation, person and location. The NER is
context-dependent and the extraction is
accomplished by string matching if the sentence is
incomplete (Wang et al., 2006).
Pre-processing is required in information
extraction to accurately retrieve a more complex
structure which contains events and relations. The
detection and characterisation of semantic relations
between entities in the text is applicable to
information extraction of relations (Jiang, 2012).
Lanfranchi et al., (2007) proposes an extraction and
search knowledge for the aerospace industry.
Correia et al., (2011) illustrates extracting ontology
hierarchies from text by tagging, extraction of
candidate classes, identification of hyponyms and
synonyms as well as identification and
representation of taxonomic relationships.
2.4 Degradation Mechanisms
In the aerospace service domain, a number of factors
lead to deterioration of mechanical components.
These components become permanently damaged
when the threshold is exceeded. Degradation
mechanisms relate the causes to the resulting
damages (see Figures 3 and 4). For instance, wear
caused by poor lubrication, fracture caused by
induced vibration of the engine vanes and crack as a
result of oxidation. The focus of the case study was
to validate the identified synonyms of the taxonomy
of wear, corrosion, fracture, and deformation, and to
develop an ontology.
Wear is the loss of material as a result of usage over
time (Ameen et al., 2011). Lack of lubrication in a
rotating engine can lead to wear. Erosion is a form
of wear, while cavitation and rub are the causes of
Mechnical Component
Jet Engine
Fuel Pump
Gear Pump
Fuel System Fuel Filter
Has- mechanisms
wear. Wear may lead to corrosion depending on the
operating environments.
Corrosion is a chemical deterioration process
leading to material loss. Oxidation and sulphidation
can cause components degradation under high
thermal stress (Pomeroy, 2005). An example of
corrosion is rust caused by oxidation (Figure 3).
Oxidation can also result in creep which leads to
deformation of the material and eventually causes
crack or spallation. Corrosion can be uncontrollable
and irreparable.
Figure 3: Surface corrosion of metal discs.
Fracture is the result of a separation of material due
to cracking or disintegration (see Figure 4). It
reduces the functionality of a component. Fractures
may occur as a result of chemical effects, shock or
stress and increases as strain rate increases.
However, deformation happens by reason of Creep
which is a slow growth caused by an applied stress.
Other types of fracture include crack, tear, burst,
peel and split which can either be micro-crack or
macro-crack (Medjaher et al., 2012).
Figure 4: Bearing with fracture (a) outer ring failure and
(b) inner ring failure (Source: (Medjaher et al., 2012).
Deformation is the effect of a change in the
geometry or shape of a component such as
shrinking, stretching, bending, and twisting due to
cumulative strain on a component when force is
applied. Deformation is either time dependent or
time independent mechanisms (Norman, 2013). In
Creep deformation the component gradually
accumulates over time with the presence of high
temperature and thermal cycles stress until the
product fails. Elastic deformation results from
applied stress on an asset which returns to its
original condition when the stress is removed.
Plastic deformation occurs when a component
exceeds its elastic limit (threshold) and results in a
permanent change to the physical structure of the
item even when stress is removed.
Degradation mechanisms result from a
combination of mechanical, electro-chemical,
operational, and environmental conditions. In
grouping the identified concepts, an understanding
of failure modes and effects analysis (FMEA) as a
measure for qualitative analysis is required (Rausand
and Høyland, 2004). The FMEA is a procedural
method to identify possible failures in a design, an
assembly or a manufacturing process, a product or a
service. Failure modes are means by which things
fail or defects occur and can be potential or actual.
Effects analysis examines and helps to understand
the consequences of the failures. The aim of the
FMEA is to take actions to reduce failures,
beginning with the highest-priority failures. Failures
can be prioritised by analysing the severity of the
consequences, the frequency of occurrence and ease
of detection. In this paper, classification of failures
is presented as classes and subclasses of knowledge
in the domain ontology (Figure 2).
This case study focuses on mechanisms reported
by service engineers in the form of text in MS word,
Excel, etc. The goal of this validation process is to
ensure the mechanisms or damage recorded in the
event reports are recognised by the information
extraction tool.
Current and future research activities in these
areas include evaluation of these applications in
large scale datasets assuming an increased
requirement for KM (Dadzie et al., 2009) which
include the study of different methods of concept
mapping and analysis to identify differences
between feature combination and integration (Zhang
et al., 2011). This includes investigative approaches
for automatic mapping (Liu et al., 2007).
This paper is based on the validation of taxonomy
of the degradation mechanisms which is a list of
terms. This is essential in deciding allocation of the
mechanisms to create a robust data collection hub.
The data collected are required to enrich Service
Knowledge and performance feedback to policy
makers. The methods used in addressing this work
include literature research, observations, and
interview with domain experts.
This study shows how to extract concepts for
ontology development to aid knowledge sharing and
reuse. The information will be pre-processed and
filtered from the raw ‘on-demand’ data sets of
textual information. These data contain various
keywords (terms and concepts) which will be
systematically and hierarchically arranged for use in
the engineering service domain.
The data will be grouped into classes of
degradation mechanisms which include fracture,
wear, corrosion, deformation and causes. The ‘AS-
IS’ and ‘TO-BE’ framework using an iterative
process before, during, after updates and sanity
check technique will be implemented.
The case study is essential to enhance Service
Knowledge by equipping service engineers with
tested, trusted and approved ontology whilst
analysing a vast amount of textual failure data. This
is important because the relevant failure data will be
validated against history information to ascertain
through-life performance of the components. This is
useful for service engineers when reporting failures
encountered in engine maintenance.
This work examines events associated with
engine component testing and the means by which
the records are processed. A series of trials and
processing updates were introduced to a framework
to deliver a proposed solution to capture concepts
from observed failure. The failure information
examined contains the engine name and type, events
or issues encountered by the component, year of
manufacture and date, and mechanism stating the
type of degradation experienced during the test.
An acquisition of a large amount of data from
engine information recorded various types of failure
modes, mechanisms and types of component feature.
The mechanisms and causes of degradation were
assessed and analysed in order to understand how
the data would be extracted. The FMEA will be used
to gain an understanding and assessment of the type
of damage present in the textual information. The
dataset is a collection of recorded issues observed,
evaluated, decided, tracked and closed by service
engineers in the maintenance domain. Mechanisms
such as bent, shrink, and crack are considered
keywords (synonyms). The synonyms of
mechanisms are manually identified, extracted from
the text and entered onto the ontology.
The taxonomy refers to the synonyms of
degradation mechanisms for existing ontology. The
procedure to modify the ontology requires an
understanding of the process, meaning of the types
of damage and under what class it should be. The
keywords are uploaded onto the system for a rerun
and re-analysis with an embedded recognition tool.
This paper shows the method to identify and collect
concepts and synonyms using the recognition tool.
3.1 Keywords Grouping
This case study illustrates the process of keyword
identification and grouping. The identification
process includes:
Define and seek specific meaning to a
degradation mechanism to ensure better
understanding of the taxonomy of the
degradation and causes (Table 1)
Attempt to ask and answer questions to ascertain
whether the identified keyword is relative to a
specified category of degradation mechanisms
(Table 3)
Identify, assess and filter degradation
mechanisms based on material loss, separation,
geometry change and property maintained in
order to predict or determine (using a flowchart)
whether the material under investigation is
affected by either corrosion, wear, deformation
or fracture. The meaning of the mechanisms
must be understood (Figure 5)
Table 1: Sample concepts and meanings.
Class Subclass Definitions
Altered from an originally
straight or even condition
Damage showing lines on
the surface of having split
without coming apart.
Material loss
Scrape or wear away by
friction or erosion
Material loss
A raised bubble, as on a
painted or laminated surface
Table 2: Sample of concepts and questions.
Class Subclass Questions
Is the material altered from
its original condition?
Is there a separation within
the material?
Is there a scrape on the
Are there raised bubbles
on the material?
As shown in Figure 5, the process starts with
observing the issue with the material in terms of
loss, change in shape and properties, and questions
to classify the defects.
Figure 5: Flowchart to determine degradation mechanisms.
3.2 Risk Matrix with Failure Modes
In order to build a robust ontology that can deliver
better performance, various probable states of events
and their consequences should be considered. These
events are described within the FMEA. Performance
of materials is affected by some event which are
termed critical. The critical events are differentiated
by the keywords used. The keywords are identified
and extracted from the FMEA.
To achieve an increased understanding of the
concepts and classification within the ontology, risk
factors applied to the identified keywords are based
on the level of seriousness of the damage and
severity of consequences of failure. The severity of
the mechanisms results from the understanding of
the FMEA procedure in analysing failures. Hence, a
relationship was created between the taxonomy of
degradation mechanisms and the severity of the
failure modes to generate an ontology for problem-
solving and decision making.
The identification of potential failure modes on
the lowest level of damage and upward hierarchy is
a representation of the ontology. The severity of
failure modes is classified into minor, major, critical
and catastrophic. Minor failure does not degrade the
overall performance beyond acceptable limits. Major
failure will degrade the system beyond acceptable
limits, but can be adequately controlled by alternate
means. Critical failure will degrade the component
further than the acceptable limits and create a safety
threat. A catastrophic failure could result in
preventing performance of the intended operations
(Rausand and Høyland, 2004).
Physical or chemical processes can lead to
events which cause the lowest level of arrangement
of failure mechanisms such as deformation, fracture,
corrosion and wear represented in Figure 6. Failure
rates for each failure mode are recorded. The failure
rates are further classified according to frequency of
occurrence to give a better understanding of
causality for informed decision making (Table 3).
Table 3: Failure rate categorisation (adapted from source:
(Rausand and Høyland, 2004).
No Failure Rate Occurrence
1 Very unlikely Once per 1000 years / more
2 Remote Once per 100 years
3 Occasional Once per 10 years
4 Probable Once per year
5 Frequent Once per month / more often
The Failure rate is different for various
operational domains with respect to a failure mode.
The FMEA contains information useful for
operation and maintenance. The risk is the severity
of the consequences mapped against failure rate.
Table 4: Risk matrix showing different failure modes
(adapted from source: (Rausand and Høyland, 2004).
Severity Categories (Consequences)
Failure rate
Remote Occas-
Probable Frequent
X    
 X   
  X  
    X
As indicated in Table 4, however, a minor damage
(e.g a Spall) to a critical component (bearing) can be
catastrophic, in that it hinders the performance of the
entire system. The failure rate and the severity
categories show that the catastrophic failure is very
unlikely to occur because the relationship within the
ontology is properly developed and utilised by
reason of the system, subsystem and component
levels Figure 6.
Figure 6: The relationship between failure cause, failure
mode and failure effect. (Adapted from Source: (Rausand
and Høyland, 2004).
This analysis informs decision making when seeking
to consider the choice to either scrap or continue to
use the component under investigation. This link is
created to assist in detecting failure mechanisms
easily based on the approved and agreed threshold. It
relates to the use of the monitored operating and
maintenance information as inputs to determine
through-life performance in terms of remaining
useful life of the component under investigation by
observing geometry, property loss and material loss
(Okoh et al., 2014).
3.3 AS-IS and TO-BE Framework
The ‘AS-IS’ and ‘TO-BE’ state is a business process
model adapted from (Q-BPM, 2014). It serves as a
guide to help understand where we are, where we
need to be and how to get there. Applying the
framework of this research – it is the current state of
the ontology, the desired robust ontology and what
to do to get the ontology to the desired robust state.
This systematic process is iteratively executed.
The findings are feasible using the proposed
‘AS-IS’ and ‘TO-BE’ process model (Figure 7). The
model was chosen based on the knowledge of the
proposed and agreed solution.
This model is implemented to bridge the gap
between ‘AS-IS’ and ‘TO-BE’ by way of process
improvement. Advantages of the framework include
planning, continuous improvement, knowledge
retention and learning, process visualization,
training, audit and compliance (Q-BPM, 2014).
The original data set (knowledge representation)
is the ‘AS-IS’ which needs to be updated and
maintained. The proposed knowledge representation
is the ‘TO-BE’. The ‘AS-IS’ model will be updated
with the identified entries (new additional concepts)
and the results presented - the amount of concepts
returned in terms of success rate.
As indicated in Figure 7, each stage addresses a
task. The process is planned, to know exactly what
to extract and how it should be addressed, check by
comparing both current and future states for the
taxonomy of degradation mechanisms, then act by
agreeing and implementing the results.
Figure 7: The AS-IS and TO-BE Framework.
3.3.1 Procedure for Analysis
The existing records are event reports presented in
Excel. In order to analyse the records, the concepts
must be identified. The procedures to analyse
information in line with the architecture in Figure 8
are as follows:-
1. Before Update - when results and degradation
process are initially processed to capture
mechanisms (see Figure 9)
2. During Update - current state when the results
and the degradation process are manually checked to
find the number of precise and accurate mechanisms
3. After Update – when the results and the
degradation processes are checked against event
information to identify mechanisms in ‘during
update. The concepts which the recognition tool
failed to capture are updated within the ontology and
then uploaded to take effect for the next ‘trial run’.
Note: The Excel file should be closed and reopened.
The recognition tool automatically runs in the
background to effectively update changes.
As illustrated in the architecture in Figure 8, the
Corpus is the application domain in Excel. The
metadata (information about the identified data),
Concept (similar or alternate keyword (synonym) in
the metadata) to feature (the specific data) and
Message (the selected information to extract from)
are represented in the event information section with
the related mechanisms / defect types. The mining of
data with the recognition tool is done in the Data
Extraction section. The section returns the results,
while the update is when the ontology is amended
with any newly found concepts.
Figure 8: The architecture to extract and analyse data.
3.3.2 Sanity Check Procedure
This sanity check ensures data integrity (Boritz,
2005). The sanity check technique in this context is
the manual count of concepts identified, captured
and stored as a taxonomy in the ontology. The data
extracted from the event information should be
accurate and consistent irrespective the number of
times the tool is implemented as long as the
ontology is updated accordingly with captured
concepts. The audit is done on the update section as
presented within the architecture in Figure 8.
The sanity check is physically counting the
concepts and by running the embedded Recognition
tool. The procedures for the technique are as
1. TR Right - when the extracted concepts from the
event information are correct. An example is ‘fret’.
2. TR Miss - refers to the concepts not captured by
the recognition tool but are correct. The concepts are
identified from failure events and fixed by adding
the same onto the ontology e.g ‘frozen’.
3. False Positive (FP) - when there is an extraction
of an incorrect concept in the event information. The
fix for the FP is removal of the concept in the
taxonomy e.g ‘close’.
4. Human error - when there is a misspelling of
actual concepts. The recognition tool will not
identify and capture it. For instance ‘luse’ instead of
‘lose’, to fix this, the word ‘luse’ is added in the
ontology. The reason for this is because service
representatives report events from different locations
in the world and typing mistakes are bound to occur,
but it is advisable to train the tool to extract ‘luse’.
Figure 9: Manually identified mechanisms.
Concepts TR Missed: The number of concepts which
the recognition tool missed frozen, wetting,
degradation and overheat. As indicated in Figure 9,
the concepts missed are manually identified and
captured onto the ontology, while Figure 8 illustrate
the identification process.
Concepts False Positive (FP): The number of
concepts which returned as FP is four (4), e.g ‘mark,
markings, mark and another mark’. These words are
not concepts, hence, should not be captured by the
recognition tool.
Mechanism DP Event Information TR
DP Speed validation,
shutdown made
causes a frozen of
the component
A degradation of
the rail due to
stretch resulting
from overheat
* FP: False Positive; HE Human; DP Deterioration process
Data Extraction
Update Message
The ontology is updated with the newly identified
concepts. The ontology is uploaded and the analysis
is repeated. The manually identified concepts were
captured by the recognition tool as indicated in
Figure 9. The number of mechanisms which
appeared in the TR right column means the sanity
check rightly identifies the concepts captured by the
tool as shown in the first and third columns.
Figure 10: Analysed data with identified mechanisms.
As indicated in Figure 10, the analysed data
returned the expected concepts. This is feasible
because the tool had been trained to identify and
capture the new concepts. That is, after update and
run of the ontology, the concepts found.
A case study in the area of taxonomy of degradation
mechanisms allows for initial evaluation of the
effectiveness of the process. Maintenance event
information was used in this research. The
recognition tool developed in Java enhanced the
extraction process.
In the first experience, one hundred rows of
records were selected and examined. The rows were
manually analysed by the researchers, who manually
identified the mechanisms. There was a manual
comparison with the results found on the
information extraction tool.
The results show the current and future states
(‘AS-IS’ and ‘TO-BE’) of the ontology. While the
‘AS-IS’ is an ill-structured presentation of keywords
anywhere in the ontology module, the ‘TO-BE’ is a
well-structured representation of the taxonomy of
the degradation mechanisms within the ontology
module. The ‘TO-BE’ is a proposed and agreed
structural arrangement by policy-makers. A high
level illustration of the ‘TO-BE’ is presented in
Figure 11 as deformation, wear, fracture and
corrosion. Table 5 shows a comprehensive final
taxonomy of the ‘TO-BE’ ontology.
Figure 11: Final Taxonomy of Degradation Mechanisms
for the TO-BE Ontology.
The presence of exclusion is needed in certain
elimination process. The exclusion is implemented
with the symbol ‘!!’. Two approaches were
attempted to fix FP issues: the first is the exclusion
of concepts in the ontology module and secondly,
classify in another ontology module. Both are good,
but in term of clear-cut representation and
performance, the latter is better.
A total 2420 event records were analysed. First,
the number of concepts ‘contained’ and ‘NOT
contained’ in the event information is based on the
existing ‘AS-IS’ structure. The event information
was interrogated with the extraction tool. The
outcome of the number of concepts and the
percentage is presented.
At the start of the process, the values were 499 as
‘contained’ concepts and 1921 as ‘NOT contained’.
That is a 21 to 79 percentage of the total records. At
this stage, the ontology was refined and updated
with concepts found in the event information.
To confirm the validity of the process, the first
97 records were selected. 31 rows ‘contained’
concepts while the 66 rows did ‘NOT contained’
concepts. The 97 records were ‘sanity checked’ to
ensure concepts were correctly recognised. The
outcome was 12 rows were blank with null
keywords. Amongst the 85 records analysed, the
domain expert identified some concepts which the
recognition tool missed and added.
The records were continually iteratively
processed by identifying new concepts and updating
the ontology. While the amended ontology was
uploaded and run, the outcome shows a massive
improvement when compared to the results of the
initial process. The outcome is based on the initial
startup of the system and software. Whereas 892
mechanisms ‘contained’ were filtered, which is
37.9%, the 1528 mechanisms ‘not contained’ were
observed, which is 63.1%. The total records of rows
analysed were 2420.
The researchers used the last 66 records to
Deformation Wea
Mechanism DP Event Information TR
DP Speed validation,
shutdown made
causes a frozen of
the component
A degradation of
the rail due to
stretch resulting
from overheat
* FP: False Positive
HE Human
DP Deterioration
ts found
Degradation Mechanisms
‘double-check’ the validity of the techniques and the
effectiveness of the ontology. The number of
concepts identified is counted manually before the
first run of the ontology. However, based on the 66
records analysed by manually checking and
counting, 67 concepts were identified instead of 63,
including 4 false positives and 7 false negatives
missed by the recognition tool. After the update of
the AS-IS ontology, the 7 false negative concepts
which resulted in 70 identified concepts.
The results were also compared with adopting a
precision, recall and F measurement for performance
evaluation of the field of information retrieval (Liu
et al., 2007; Dellschaft and Staab, 2006). Also, in
ascertaining the extraction performance for learning
based on the manual identification regarding
Precision and Recall (Sabou et al., 2005).
Precision = mechanisms_found/All_expected
Recall = mechanisms_found/All_mechanisms_found
F = (2 x Precision x Recall)/(Precision + Recall)
where, mechanisms_found is the number of
keywords returned, All_expected is the total
expected keywords returned; All_mechanisms_found
total keywords found. The precision of 94%, recall
of 90% and F measurement of 92%.
Ontology pruning and refinement (Maedche and
Volz, 2001) are introduced at this stage. Pruning
(Sabou et al., 2005), is removing irrelevant concepts
in the ontology, that is 34 assumed old concepts
which are classified into a different ontology
module. The refinement is effective by the upload
and run of the TO-BE ontology with the relevant
concepts based on the new classification. This
refinement accurately and precisely retains the
newly identified and the existing concepts regarding
the application domain. A total of 42 concepts
returned. The success rate is based on the ‘After
Update’ of the agreed ‘TO-BE’ ontology.
In using the Ontology building support (Maedche
and Volz, 2001), the ontology precision model
would be
(Returned + found) / (Returned + found + old)
Where Returned is the concepts based on agreed
‘TO-BE’ ontology by the domain expert, found is
the concepts not considered during the building of
the ontology and old are the irrelevant concepts
Table 5: Final Taxonomy of the TO-BE Ontology.
Deterioration process
Deterioration Degradation
Mechanical deterioration
Corrosion Deformation Fracture Wear Cosmetic
Location Containment Material
Anti-corrosion Deformed Burst Worn Blueing Friction Fouls Leak Brittled
Burnt Bent Cracked Abraded Polished Fatigue De-bond Drip Embrittled
Builtup Cut Brinelled Tarnished Oscillated Clashed Weep Hardened
Oxidised Deposited Perforated Cavitated Bruised Resonated Contacted Lost Softened
Pitted - corrosion Bulged Disintegrated Chaffed Burnished Hit Debonded Misfilled Coked
Stress corrosion Collapsed Ruptured Eroded Stained Bumped Ratcheted Breakout Glazed
Rusted Shingled Snapped Scrape Streaked Banged Released Spill Dealloyed
Sulphidated Compressed Divided Frayed Discoloured Wiggled Separated Melted
Elongated Split Fretted Discolored Vibrated Delaminate
Scorched Extruded Flaked Lumped Strained Slipped
Thermal erosion Distorted Punctured Galled
Stressed Displaced Blocked
Flattened Spalled Picked up Fire Pooled Clogged Creeped
Shrunk Blistered Roughened Ingested Hide Starvation Frozen
Twisted Peeled Plucked Injestion Dislocated Short circuit
Stretched Wrecked Scalloped Ingress Misaligned Jammed
Burred Sheared Material transfer Misassembled Seized
Battered Lifted Plowed Damaged Misclocked
Dented Broken Exfoliation Overloaded Mismatched
Depressed Fragmented Scuffed Overspeed Misfitted
Dimpled Chipped Rubbed Overpressure
Lapped Creviced Overfill
Indented Torn Contaminated
Nicked Iced
Grooved Overheat
Gouged Bruise
Scored Unbalanced
Foreign object
damage (fod)
removed from the ontology module. From this
exercise, it is observed that the higher ratio results in
better support for ontology development. The
precision reveals 55% of relevant instances
retrieved. The result shows a significant pass rate
compare with ‘AS-IS’ ontology of concepts. This is
dependent on the application domain and the
relevant concepts in the corpus is training the
software to learn.
This paper demonstrates the verification and
validation of the taxonomy of the degradation
mechanisms based on the sanity check technique.
The analysis of the framework is validated by
manual identification, capture and counting of
individual concept. The relevance of this taxonomy
is to improve service knowledge.
The iterative sanity check technique was useful
for the practical task carried out to audit and certify
the current ontology. The same technique applies to
the classification of newly identified concepts.
The case study focuses on the types of damage
observed by service engineers and classified
hierarchically in accordance with the predominant
degradation mechanisms. The validation process can
be used in the audit of information systems.
This research can help other service related
application here access to historical information is
essential, e.g. predicting system failure and spare
parts planning.
The future work within this project involves
developing a novel remaining useful life prediction
using both current health information and history of
a component.
The idea presented in this paper relies on the
PLAN, DO, CHECK and ACT (PDCA) business
process model (Q-BPM, 2014). The PDCA cycle
involves continuous management activities to
support decision making. It is an iterative operation
observed as sanity checks. Techniques such as a
workflow diagram can be used. With constant
review and the addition of new degradation
mechanisms, the efficiency, effectiveness and
performance of the ontology is improved.
This paper discussed how the ‘TO-BE’ ontology
structure was developed. Classification is on the
basis of the most common or predominant type of
degradation experienced by mechanical components.
However, a significant difference in results between
previous analysis, TO-BE’ ontology and the amount
of keywords which were categorised into another
ontology module. There is an improvement as the
precise concepts captured were retrieved.
Furthermore, compound words (‘fire detector’,
‘fire wire’) can be excluded in the ontology in order
to prevent redundancy of concepts. The iterative
process this paper can be used in parallel with the
‘AS-IS’ and ‘TO-BE’ framework for effective and
efficient execution of tasks in a sequential manner.
This work is funded by the EPSRC (Engineering and
Physical Sciences Research Council, UK), grant
number EP/I033246/1 delivered by EPSRC Centre
for Innovative Manufacturing in Through-life
Engineering Services. The authors also acknowledge
Rolls-Royce PLC for their support in this research.
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