Fan sound fault detection algorithm based on the fusion of convolutional neural network and gated recurrent unit network

Feng Qiu; Shengbing Chen; Dou Jin; Anqi Lu

doi:10.1117/12.3004437

16 August 2023 Fan sound fault detection algorithm based on the fusion of convolutional neural network and gated recurrent unit network

Feng Qiu, Shengbing Chen, Dou Jin, Anqi Lu

Author Affiliations +

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127870P (2023) https://doi.org/10.1117/12.3004437
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Fans are the important equipment in the manufacturing industry, which are easy to cause faults because of the complex working conditions and high speed. Various fault detection algorithms based on electrical, vibration and video have been proposed, but these algorithms are difficult to meet the detection requirements of enterprises. In this paper, a novel fan fault detection algorithm is presented based on sound signal detection technology. According to the different characteristics of the fan sound signal in the time and frequency domains, we combined the convolutional neural networks (CNN, which is sensitive to spatial information) and the gated recurrent unit (GRU, which is sensitive to timing information), and proposed a fan sound fault detection algorithm. Firstly, according to the variation characteristics of the acoustic signal in different states, the spectrogram of sound signal containing spatial and temporal characteristics is extracted, and the histogram equalization is used to enhance the spectrogram. On this basis, CNN and GRU are combined to train the Log-Fbank spectrogram respectively, so that it can better model the different characteristics of time domain and frequency domain information, so as to obtain a fan state model with high confidence and realize the sound fault detection of the fan. Experiments on MIMII dataset show that the algorithm has high fault recognition accuracy and fast convergence speed, which has good application value.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Feng Qiu, Shengbing Chen, Dou Jin, and Anqi Lu "Fan sound fault detection algorithm based on the fusion of convolutional neural network and gated recurrent unit network", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127870P (16 August 2023); https://doi.org/10.1117/12.3004437

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KEYWORDS

Fluctuations and noise

Detection and tracking algorithms

Image enhancement

Education and training

Feature extraction

Signal detection

Evolutionary algorithms

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