Vol.6, Special Issue, 2007 pp. 185-196
UDC 007.52:004.896(045)=111
PARAMETER ESTIMATION FOR ONLINE CONDITION
MONITORING OF ROBOTIC MACHINES
Tsz Ming James Hui, Honghai Liu, David J. Brown
Institute of Industrial Research, University of Portsmouth Burnaby
Building, Portsmouth PO1 3QL, England, United Kingdom
e-mail: Honghai.Liu@port.ac.uk;
David.Brown@port.ac.uk
Abstract. This paper proposes a novel learning approach to online
condition monitoring of robotic machines. The real-time learning process
comprises three stages, domain knowledge defining, random learning and
ordinal learning. Domain knowledge defining abstracts the model of a robotic
machine; random learning and ordinal learning stages train the parameters
of the abstract model with random data selection and ordinal data selection,
respectively. Simulation results have proved that the pro-posed method
is efficient and feasible for online fault diagnosis of robotic machines.
Key words: Online machine learning, fault diagnosis and machine
monitoring
PROCENA PARAMETARA ONLAJN USLOVA MONITORINGA
ROBOTSKIH MAŠINA
Ovaj rad predlaže novi pristup u onlajn učenju uslova monitoringa robotskih
mašina. Realno vreme procesa učenja sastoji se iz tri faze, domena odredjivanja
znanja, slučajnog učenja i podregjenog učenja. Odredjivanje domena znanja
sažima model robotske mašine; faze slučajnog učenja i podregjenog učenja
utiču na parametre sažetog modela sa slučajno odabranim podacima i podregjenog
odabranim podacima ponaosob. Rezultati simuliranja dokazuju da je predloženi
metod efikasan iI izvodljiv za onlajn dijagnozu grešaka robotskih mašina.
Ključne reči: Onlajn učenje, mašinsko učenje, dijagnoza
grešaka, monitoring robotskih mašina
