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research-article

Virtual sensors for erroneous data repair in manufacturing a machine learning pipeline

Authors:

Erik Johannes Husom,

Dimitra Politaki,

Simeon Tverdal,

Nicolas JourdanAuthors Info & Claims

Volume 149, Issue C

https://doi.org/10.1016/j.compind.2023.103917

Published: 01 August 2023 Publication History

Abstract

Manufacturing converts raw materials into finished products using machine tools for controlled material removal or deposition. It can be observed using sensors installed within and around machine tools. These sensors measure quantities, such as vibrations, cutting forces, temperature, currents, power consumption, and acoustic emission, to diagnose defects and enable zero-defect manufacturing as part of the Industry 4.0 vision. The continuity of high-quality sensor data streams is fundamental to predicting phenomena, such as geometric deformations, surface roughness, excessive coolant use, and imminent tool wear with adequate accuracy and appropriate timing. However, in practice, data acquired by some sensors can be of poor quality and unsuitable for prediction due to sensor faults stemming from environmental factors. In this paper, we answer if we can repair erroneous data in a faulty sensor based on data simultaneously available in redundant sensors that observe the same process. We present a machine learning pipeline to synthesize virtual sensors that can step in for faulty sensors to maintain reasonable quality and continuity in sensor data streams. We have validated the synthesized virtual sensors in four industrial case studies.

Highlights

•

The high-quality sensor data streams are fundamental to predicting phenomena.

•

Data acquired by some sensors can be of poor quality and unsuitable for prediction.

•

Virtual sensors can step in for faulty sensors.

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Cited By

Husom ESen SGoknil ABosch JLewis GCleland-Huang JMuccini H(2024)Engineering Carbon Emission-aware Machine Learning PipelinesProceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644943(118-128)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644815.3644943
Sen SHusom EGoknil ATverdal SNguyen P(2024)Uncertainty-Aware Virtual Sensors for Cyber-Physical SystemsIEEE Software10.1109/MS.2023.330687341:2(77-87)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/MS.2023.3306873
Choi JKim DChung MKim GPark HLim S(2024)Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drillingExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124656255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124656
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Information & Contributors

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Published In

cover image Computers in Industry

Computers in Industry Volume 149, Issue C

Aug 2023

239 pages

ISSN:0166-3615

Issue’s Table of Contents

The Author(s).

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 August 2023

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Cited By

Husom ESen SGoknil ABosch JLewis GCleland-Huang JMuccini H(2024)Engineering Carbon Emission-aware Machine Learning PipelinesProceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI10.1145/3644815.3644943(118-128)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644815.3644943
Sen SHusom EGoknil ATverdal SNguyen P(2024)Uncertainty-Aware Virtual Sensors for Cyber-Physical SystemsIEEE Software10.1109/MS.2023.330687341:2(77-87)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/MS.2023.3306873
Choi JKim DChung MKim GPark HLim S(2024)Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drillingExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124656255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124656
Husom ESen SGoknil ATverdal SNguyen P(2023)REPTILE: a Tool for Replay-driven Continual Learning in IIoTProceedings of the 13th International Conference on the Internet of Things10.1145/3627050.3630739(204-207)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3627050.3630739
Husom EGoknil ATverdal SSen SNguyen P(2023)Automated Behavior Labeling for IIoT DataProceedings of the 13th International Conference on the Internet of Things10.1145/3627050.3630725(174-178)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3627050.3630725
Tverdal SGoknil ANguyen PHusom ESen SRuh JFlamigni F(2023)Edge-based Data Profiling and Repair as a Service for IoTProceedings of the 13th International Conference on the Internet of Things10.1145/3627050.3627065(17-24)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3627050.3627065
Westad FLodgaard LPedersen TKhomh FChatterjee PMetzger A(2023)Data Pre-processing and Sensor-Fusion for Multivariate Statistical Process Control of an Extrusion ProcessProceedings of the 3rd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things10.1145/3617573.3618029(9-15)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3617573.3618029
Powell DMagnanini M(2023)EditorialComputers in Industry10.1016/j.compind.2023.103962151:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.compind.2023.103962

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