Frost thickness estimation in a domestic refrigerator using acoustic signals and artificial intelligence

作者：

Highlights：

• Intelligent models for frost classification in an evaporator surface.

• Design of a concurrent data generation/acquisition system.

• The compute of sound pressure level reduces the data dimensionality.

• Acoustic signals are used to detect different frost levels.

摘要

•Intelligent models for frost classification in an evaporator surface.•Design of a concurrent data generation/acquisition system.•The compute of sound pressure level reduces the data dimensionality.•Acoustic signals are used to detect different frost levels.

论文关键词：Artificial neural networks,Refrigeration,Frost thickness,Probabilistic neural networks,Sound pressure level

论文评审过程：Received 22 October 2021, Revised 9 January 2022, Accepted 28 March 2022, Available online 5 April 2022, Version of Record 4 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117071