Air conditioner production line abnormal sound detection method and system

By combining a microphone spherical array and a panoramic camera with the HOA-SHT domain analysis framework and convolutional neural network, we have achieved efficient and accurate detection of abnormal noises from air conditioner wall units, solved the problem of foreign object noise in the impeller blades, and improved production efficiency and user experience.

CN120313951BActive Publication Date: 2025-10-24BEIJING FRYHUIER TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510466688.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-04-15
Publication Date
2025-10-24
Estimated Expiration
2045-04-15

AI Technical Summary

Technical Problem

In the current production of wall-mounted air conditioners, noise problems caused by foreign objects on the surface of the fan blades are difficult to detect efficiently and accurately through manual listening, resulting in high false detection rates, high missed detection rates, and low efficiency.

Method used

By combining a microphone spherical array and a panoramic camera with the HOA-SHT domain analysis framework and convolutional neural network, a panoramic sound image is generated. Sound signals are extracted through multimodal localization and multi-scale Mel spectrum, and abnormal noise is detected using a convolutional neural network, thus achieving efficient and accurate classification of abnormal noises from air conditioners.

Benefits of technology

It significantly improves the accuracy and efficiency of detecting abnormal noises in wall-mounted air conditioners, reduces false detection and missed detection rates, and improves production quality and user experience.

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Abstract

The application provides an air conditioner production line abnormal sound detection method and system, comprising: acquiring sound signals in real time through a microphone spherical array; processing the sound signals based on an HOA-SHT domain analysis framework to generate a panorama sound image; acquiring an air conditioner image through a panorama camera; locking an air conditioner area according to the air conditioner image; performing multi-modal positioning on the air conditioner area through the panorama sound image to obtain a target area; extracting sound signals from the target area through a multi-scale mel spectrum, and inputting the extracted sound signals into a beamformer of a neural network to obtain noise signals; detecting and identifying the noise signals based on a convolutional neural network-based anomaly classifier to obtain air conditioner abnormal sound classification results; efficiently and accurately detecting air conditioner hanging machine abnormal sounds, effectively overcoming the limitations of traditional manual detection methods, significantly improving production efficiency, reducing the false detection rate and the missed detection rate, and providing strong quality guarantee for the production line.
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