Event monitoring of coal mill operation state based on acoustic fingerprint

Kai He; Xuegang Ren; Gang Cheng; Yan Wang; Dongxian Li; Haorui Liu

doi:10.1117/12.2623427

1 December 2021 Event monitoring of coal mill operation state based on acoustic fingerprint

Kai He, Xuegang Ren, Gang Cheng, Yan Wang, Dongxian Li, Haorui Liu

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120792O (2021) https://doi.org/10.1117/12.2623427
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Coal mills are important equipment of thermal power units work in hostile environment, so the health working condition of coal mills is a key factor for the normal operation of power plants. To monitor the operation of coal mills and make early warning of fault diagnosis, it has to collect the vibration/sound signals of coal mill operation and pre-process the sound samples to extract Mel-frequency cepstrum coefficients (MFCC) as features, and use the set of MFCC to train Gaussian mixture models (GMM). Finally, it uses the trained GMM to test the operation of coal mills. The test results demonstrate that audio anomaly detection based on MFCC and GMM can be used to effectively identify the on/off status of coal mills.

Citation Download Citation

Kai He, Xuegang Ren, Gang Cheng, Yan Wang, Dongxian Li, and Haorui Liu "Event monitoring of coal mill operation state based on acoustic fingerprint", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120792O (1 December 2021); https://doi.org/10.1117/12.2623427

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