Authors:
Chandrakanth Kancharla
1
;
Jens Vankeirsbilck
1
;
Dries Vanoost
2
;
Jeroen Boydens
1
and
Hans Hallez
1
Affiliations:
1
M-Group, DistriNet, Department of Computer Science, KU Leuven Bruges Campus, 8200 Bruges, Belgium
;
2
M-Group, WaveCoRE, Department of Electrical Engineering, KU Leuven Bruges Campus, 8200 Bruges, Belgium
Keyword(s):
Condition Based Monitoring, Self Adaptation, Resource Constrained Computing, Bearing Fault Diagnosis, Domain Invariance.
Abstract:
While the current machine fault diagnosis is affected by the rarity of cross conditional fault data in practice, efficient implementation of these diagnosis models on resource constrained devices is another active challenge. Given such constraints, an ideal fault diagnosis model should not be either generalizable across the shifting domains or lightweight, but rather a combination of both, generalizable while being minimalistic. Preferably being uninformed about the domain shift. Addressing these computational and data centric challenges, we propose a novel methodology, Convolutional Auto-encoder and Nearest Neighbors based self adaptation (SCAE-NN), that adapts its fault diagnosis model to the changing conditions of a machine. We implemented SCAE-NN for various cross-domain fault diagnosis tasks and compared its performance against the state-of-the-art domain invariant models. Compared to the SOTA, SCAE-NN is at least 6− 7% better at predicting fault classes across conditions, while
being more than 10 times smaller in size and latency. Moreover, SCAE-NN does not need any labelled target domain data for the adaptation, making it suitable for practical data scarce scenarios.
(More)