Thesis (MEng)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Most industries are being forced to operate at lower costs while delivering more
outputs and ensuring a safe working environment. An opportunity to achieve
this for asset intensive industries lies within the complex and integrated field
of Physical Asset Management (PAM). This study is specifically concerned
with the maintenance subset of PAM, more specifically, the proactive maintenance
strategy. A field known as prognostics emerges when combining two
maintenance tactics, namely predictive and preventative maintenance.
Prognostics uses historical failure data from preventative maintenance and
variable readings used in predictive maintenance to estimate asset reliability.
Reliability is estimated using statistical models commonly known as reliability
models or survival models. Variable readings used must describe or portray
the health of the assets considered and are called covariates.
A problem that exists in the maintenance subset of PAM is concerned with
the data needed for the survival models. The historical failure data is difficult
to come by or non-existent in industry and the covariate data is often
noisy and inaccurate. This poses a problem when wanting to make important
maintenance decisions because the prognostics survival models require both
the historical failure data and the covariate data. The covariate data is generally
acquired by applying Condition Monitoring (CM) to assets, monitoring
characteristics reflecting the asset’s health. Prognostics can aid with maintenance decisions because once the equipment reliability has been estimated, it
is possible to predict the time that an asset can still operate at its prescribed
level of performance. This time of operation, which the asset can still operate,
is more commonly known as its residual life (RL).
To overcome this problem, six of the most popular survival models found
in literature, namely the Accelerated Failure Time Model (AFTM), Additive
Hazards Model (AHM), Proportional Covariate Model (PCM), Proportional
Hazards Model (PHM), Proportional Odds Model (POM) and the Prentice,
Williams and Peterson (PWP), are considered and populated with historical
failure data and the covariate data elicited from people. The people whom
the data is obtained from are considered as experts in the field this study is
conducted in. Also, the data is subjective because each expert has their own
opinions and judgement concerning the assets in this study. The purpose of this
study is, thus, to investigate whether subjective data can be used to populate
survival models, therefore, allowing RL predictions of the assets considered.
A guideline consisting of five steps that aid with what system variables to
consider as covariates, which people can be selected as experts and selecting
the most appropriate survival model, is created and presented. Following the
guideline, a case study is conducted on power transformers at an organization
in South Africa.
Results from the case study reveal that the PCM is the most appropriate
survival model reviewed. Using the PCM, RL predictions are made after the
models are populated with subjective data and objective industry standard
data. The results indicate that the subjective data yielded the same general
trends but less conservative estimates when compared to industry standard
data. Subjective data can, therefore, be used to populate survival models but
this is inherently risky because of the less conservative results noted from this
study. This study is based on a single case study, it does prove that it is
possible to use the subjective data as an alternative to objective data. It is
possible, however, that this characteristic does not apply for other asset types. / AFRIKAANSE OPSOMMING: Die meerderheid nywerhede word onder geweldige druk geplaas om laer bedryfskostes
te handhaaf en ter selfde tyd word dit van hulle verwag om hul
uitsette te vermeerder en ´n veilige werksomgewing te bied. Bate intensiewenywerhede
het ´n geleentheid om hierdie druk te verlig deur gebruik te maak
van ´n komplekse en geïntegreerde veld bekend as Fisiese Batebestuur (FB).
Hierdie studie is gefokus op die instandhouding onderafdeling van FB, spesifiek
die proaktiewe instandhoudingsstrategie. Twee proaktiewe instandhoudingstaktieke,
naamlik voorspellende en voorkomende instandhoudingtaktieke, word
saamgesmelt en vorm ´n veld bekend as prognostiek.
Prognostiek gebruik historiese falingdata van voorkomende instandhouding en
veranderlike aflesings vanaf toestandmoniteering toeristing gebruik in voorspellende
instandhouding om bate batroubarheid te bereken. Hierdie betroubaarheid
word bereken deur gebruik te maak van statistiese modelle bekend
as oorlewingsmodelle.
Een van die probleme wat voorkom in die instandhouding onderafdeling van
FB het te doen met die beskikbaarheid van die data wat benodig word vir
die oorlewingsmodelle. Historiese falingdata is selde beskikbaar of bestaan
glad nie en die toestandsmoniteering data is dikwels onakuraat. Prognostiek
word gebruik om belangrikke instandhoudingsbesluite te motiveer, dus is die beskikbaarheid en betroubaarheid van die nodige data van belange. Om hierdie
struikelblok te oorkom bestudeer hierdie studie die gebruik van subjektiewe
data bekom vanaf deskundiges in prognostieke oorlewingsmodelle. Die doel
van hierdie studie is dus om vas te stel of subjektiewe data gebruik kan word
in prognostieke oorlewingsmodelle.
Ses oorlewingsmodelle wat gereeld voorkom in literatuur word nagesien in hierdie
studie, die modelle sluit in die “Accelerated Failure Time Model” (AFTM),
“ Additive Hazards Model” (AHM), “Proportional Covariate Model” (PCM)
, “Proportional Hazards Model” (PHM), “Proportional Odds Model” (POM)
en die “Prentice Williams and Peterson” (PWP) model. Hierdie modelle word
aangevul deur die subjektiewe data wat onttrek is van deskundiges in ´n sekere
gebied, vir hierdie studie is die gebied krag transformators.
Met gebruik van hierdie modelle kan die betroubaarheid van die betrokke toerusting
bereken word. Sodra die betroubaarheid bereken is kan die oorblywende
lewe van die toerusting voorspel word. Die oorblywendelewe is die tyd
wat ´n stuk toerusting nog moontlik kan werk sonder om te faal. Dit is belangrik
omdat nodige instandhoudingsbesluite geneem moet word.
Hierdie studie stel ´n metode voor vir die uitvoer van die navorsing en soortgelyke
studies. Die metode dui vyf stappe aan wat voorstel watter veranderlikes
om te gebruik as kovariate in die oorlewingsmodelle, watter mense as deskundiges
gekies kan word, en hoe om die mees toepasslikke oorlewingsmodelle te
kies. Nadat hierdie metode voorgestel is word dit toegepas op krag transformators
in ´n gevallestudie wat plaasgevind het in Suid Afrika.
Vir die gevallestudie is die PCM die meestoepaslikke oorlewingsmodel. Die
oorblywende lewe voorspellings wat die metode opgelewer het is met die voorspellings
gebaseer op die industriestandaard data vergelyk. Die resultate dui
aan dat deskundiges minder konserwatiewe beramings lewer. Dus kan die
subjektiewe data gebruik word in oorlewingsmodelle maar die beramings is
minder konserwatief en daarom van natuur meer riskant. Hierdie studie se
gevolgtrekkings is gebaseer op ´n enkele gevallestudie. Dit is dus moontlik dat
die subjektiewe data dalk nie as ´n alternatief gebruik kan word met ander
tipes toerusting nie.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/97089 |
| Date | 03 1900 |
| Creators | Schoeman, Jaco |
| Contributors | Vlok, P. J., Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 161 pages : illustrations |
| Rights | Stellenbosch University |
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