用时序卷积与表征学习实时检测设备声音异常，提升工业监测可靠性。
Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection
- 融合半监督时序卷积与表征学习，构建混合模型捕捉复杂声学异常模式。
- 在公开数据集上优于现有方法，准确率显著提升。
- 适合工业设备故障早期预警场景，尤其适用于无标注数据环境。
工业机械部件的早期故障检测对保障运行可靠性和安全性至关重要，有助于实现机器状态监控（MCM）。本研究提出一种创新的实时声学异常检测方法，结合半监督时序卷积与表征学习，采用基于时序卷积网络（TCN）的混合模型策略，有效应对声学数据中多种复杂异常模式。所提模型在性能上显著优于领域内已有研究，不仅提供了量化的优越性证据，还通过t-SNE可视化等手段进一步验证了其有效性。
原文摘要 · Abstract (English)
The early detection of potential failures in industrial machinery components is paramount for ensuring the reliability and safety of operations, thereby preserving Machine Condition Monitoring (MCM). This research addresses this imperative by introducing an innovative approach to Real-Time Acoustic Anomaly Detection. Our method combines semi-supervised temporal convolution with representation learning and a hybrid model strategy with Temporal Convolutional Networks (TCN) to handle various intricate anomaly patterns found in acoustic data effectively. The proposed model demonstrates superior performance compared to established research in the field, underscoring the effectiveness of this approach. Not only do we present quantitative evidence of its superiority, but we also employ visual representations, such as t-SNE plots, to further substantiate the model's efficacy.
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