基于音频的煤矿提升机异常检测系统设计

Design of Audio-Based Coal Mine Hoist Anomaly Detection System

为进一步提高煤矿提升机异常检测能力水平,结合实际工作需求,以B/S架构搭建基于音频的煤矿提升机异常检测系统架构,并分别应用EMD-mRWR算法模型和MFEC-GCN算法模型,对音频处理和异常音频识别功能进行设计,以实现系统功能。从实验测试结果来看,该系统对于异常音频的检测准确率相对较高,因此证明本次设计的系统具有潜在应用价值。

In order to further improve the level of coal mine hoist anomaly detection ability,combined with the actual work requirements,the audio-based coal mine hoist anomaly detection system architecture is built with B/S architecture,and the EMD-mRWR algorithm model and MFEC-GCN algorithm model are applied respectively to design the audio processing and anomalous audio recognition functions to achieve the system functions.From the experimental test results,the system has a relatively high accuracy rate for the detection of abnormal audio,thus proving that the system designed in this work has potential application value.