DETECTING INDUSTRIAL FOULING BY MONOTONICITY DURING ULTRASONIC CLEANING | IEEE Conference Publication | IEEE Xplore

DETECTING INDUSTRIAL FOULING BY MONOTONICITY DURING ULTRASONIC CLEANING


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...Show More

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.
Date of Conference: 17-20 September 2018
Date Added to IEEE Xplore: 01 November 2018
ISBN Information:
Print on Demand(PoD) ISSN: 1551-2541
Conference Location: Aalborg, Denmark

References

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