Techniques of Prognostics for Condition-Based Maintenance in Different Types of Assets

Projekt:

JVTC

Sammanfattning:
Today, many maintenance programs in the industrial
sector rely on condition-based maintenance (CBM). This
type of program helps improve maintenance tasks because
machines or equipment are continuously monitored.
Condition-based maintenance recommends maintenance
decisions based on information collected through condition
monitoring. It consists of three main steps: data acquisition,
data processing and maintenance decision-making.
Prognostics is a key feature of today’s maintenance
strategies; it prevents inopportune maintenance spending,
because with prognostics, we can estimate the remaining
useful life (RUL) and minimise maintenance tasks.
Real prognostic systems are scarce in industry. For one
thing, it is difficult to choose an efficient technology, as
there are many possible approaches: model based, data
driven and experience based. The applicability of each is
dependent on industrial constraints. Thus, the general
purpose of the present work is to review the various
techniques of prognosis for different industrial assets. It
investigates each approach to determine which techniques
are applicable to different assets (rotating machines,
esructutas and complex systems). Finally, it compares the
approachs and their respective techniques in a table.

Länk till publikation i fulltext (pdf-fil, 1 234,6 kB. Öppnas i nytt fönster)

Författare: Angel Hernandez ; Diego Galar
Utgivare: Luleå tekniska universitet
Utgivningsdatum: 2014
Diarienummer: TRV 2011/58769
ISBN: 978-91-7439-980-6
Antal sidor: 130
Språk: Engelska
Kontaktperson: Per Olof Larsson Kråik, UHjbs