Lightweight and Self Adaptive Model for Domain Invariant Bearing Fault Diagnosis

Chandrakanth Kancharla, Jens Vankeirsbilck, Dries Vanoost, Jeroen Boydens, Hans Hallez

2023

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.

Paper Citation

in Harvard Style

Kancharla C., Vankeirsbilck J., Vanoost D., Boydens J. and Hallez H. (2023). Lightweight and Self Adaptive Model for Domain Invariant Bearing Fault Diagnosis. In Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-643-9, SciTePress, pages 29-38. DOI: 10.5220/0011822700003482

in Bibtex Style

@conference{iotbds23,
author={Chandrakanth Kancharla and Jens Vankeirsbilck and Dries Vanoost and Jeroen Boydens and Hans Hallez},
title={Lightweight and Self Adaptive Model for Domain Invariant Bearing Fault Diagnosis},
booktitle={Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2023},
pages={29-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011822700003482},
isbn={978-989-758-643-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Lightweight and Self Adaptive Model for Domain Invariant Bearing Fault Diagnosis
SN - 978-989-758-643-9
AU - Kancharla C.
AU - Vankeirsbilck J.
AU - Vanoost D.
AU - Boydens J.
AU - Hallez H.
PY - 2023
SP - 29
EP - 38
DO - 10.5220/0011822700003482
PB - SciTePress