用声音检测木工刨床故障，提升新手操作员的诊断能力。
Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers
- 用带跳跃连接的卷积自编码器分析设备声音异常
- 在真实工厂数据上达到0.875的ROC曲线下面积
- 适合工业场景中的设备故障预警与智能运维
近年来，木材加工行业面临熟练工人短缺问题，导致设备突发故障频发，增加运营成本。锯木厂环境恶劣，对机械和传感器构成挑战。有经验的操作员可通过声音判断设备缺陷或故障，因此利用声学监测辅助新手操作是一种可行方案。本文基于真实工厂环境采集的木工刨床音频数据，探索使用深度卷积自编码器实现声学异常检测。提出带有跳跃连接的卷积自编码器（Skip-CAE）及结合变压器结构的改进模型，在DCASE自编码器基线、一类SVM、孤立森林以及已发表的卷积自编码器架构上均表现更优，分别获得0.846和0.875的ROC曲线下面积。实验表明，引入跳跃连接和注意力机制可进一步提升异常检测性能。
原文摘要 · Abstract (English)
In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.
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