Detecting Industrial Fouling by Monotonicity during Ultrasonic Cleaning

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http://hdl.handle.net/10138/324414

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Rajani , C , Klami , A , Salmi , A , Rauhala , T , Haeggström , E & Myllymäki , P 2018 , Detecting Industrial Fouling by Monotonicity during Ultrasonic Cleaning . in 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) . IEEE , IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING , Aalborg , Denmark , 17/09/2018 . https://doi.org/10.1109/MLSP.2018.8517080

Title: Detecting Industrial Fouling by Monotonicity during Ultrasonic Cleaning
Author: Rajani, Chang; Klami, Arto; Salmi, Ari; Rauhala, Timo; Haeggström, Edward; Myllymäki, Petri
Contributor: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Physics
University of Helsinki, Altum Technologies
University of Helsinki, Department of Physics
University of Helsinki, Department of Computer Science
Publisher: IEEE
Date: 2018-11-01
Language: eng
Number of pages: 6
Belongs to series: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
ISBN: 978-1-5386-5478-1
978-1-5386-5477-4
URI: http://hdl.handle.net/10138/324414
Abstract: High power ultrasound permits non-invasive cleaning of industrial equipment, but to make such cleaning systems energy efficient, one needs to recognize when the structure has been sufficiently cleaned without using invasive diagnostic tools. This can be done using ultrasound reflections generated inside the structure. This inverse modeling problem cannot be solved by forward modeling for irregular and complex structures, and it is difficult to tackle also with machine learning since human-annotated labels are hard get. We provide a deep learning solution that relies on the physical properties of the cleaning process. We rely on the fact that the amount of fouling is reduced as we clean more. Using this monotonicity property as indirect supervision we develop a semi-supervised model for detecting when the equipment has been cleaned.
Subject: 113 Computer and information sciences
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