Abstract Monitoring acoustic emissions (AE) is an effective way to identify coal deformation and destruction processes. It is therefore of great significance to analyze the characteristics of AE during coal destruction process. This paper applies the Mel frequency cepstrum coefficient (MFCC) approach of automatic speech recognition (ASR) to analyze the characteristics of the AE of coal. The MFCC of AE within 40 ms during the uniaxial compression failure of 55 coal samples was extracted. The results show that the MFCC changes regularly with increasing stress on the coal sample, which changes from the beginning to the end of loading. The ratio of stress to the compressive strength of the coal sample is defined as the stress state of the coal sample and the correlation between MFCC and the stress state of the coal sample is analyzed. MFCC-3 (the third parameter of MFCC) and MFCC-6 (the sixth parameter of MFCC) match the linear change relationship at the relevant stress state. The distribution characteristics of MFCC-3 of 55 coal samples under the same stress state showed that the parameter value is normally distributed under the same stress state. If MFCC-3 is less than -2.481, the probability that stress will reach 90% of its ultimate strength exceeds 93.8%, and the probability of coal failure exceeds 50%. This study shows that the feature extraction method in the field of ASR can be used for the AE feature analysis of the deformation and destruction processes of coal samples, and the extracted MFCC of AE can be used to evaluate their safety state. These results are of great significance to further advance the analysis of the characteristics of the AE of coal.

中文翻译：

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用自动语音识别方法分析煤层故障的声发射特性

摘要 监测声发射（AE）是识别煤变形和破坏过程的有效方法。因此，分析煤破坏过程中AE的特征具有重要意义。本文应用自动语音识别（ASR）的梅尔倒谱系数（MFCC）方法来分析煤的声发射特征。提取了 55 个煤样单轴压缩破坏过程中 40 ms 内 AE 的 MFCC。结果表明，随着煤样应力的增加，MFCC有规律地变化，从加载开始到结束都发生变化。将煤样的应力与抗压强度的比值定义为煤样的应力状态，分析了MFCC与煤样应力状态的相关性。MFCC-3（MFCC的第三个参数）和MFCC-6（MFCC的第六个参数）在相关应力状态下匹配线性变化关系。55个煤样在相同应力状态下的MFCC-3分布特征表明，参数值在相同应力状态下呈正态分布。如果MFCC-3小于-2.481，则应力达到其极限强度90%的概率超过93.8%，煤破坏的概率超过50%。本研究表明，ASR领域的特征提取方法可用于煤样变形破坏过程的声发射特征分析，提取的声发射的MFCC可用于评价其安全状态。这些结果对进一步推进煤的AE特征分析具有重要意义。