Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

TL;DR

This paper presents a deep convolutional autoencoder with skip connections and transformer mechanisms for acoustic anomaly detection in industrial wood planers, improving detection accuracy on real-world factory data.

Contribution

It introduces a novel convolutional autoencoder architecture with skip connections and attention mechanisms tailored for acoustic anomaly detection in wood processing machinery.

Findings

Achieved an AUC of 0.846 with Skip-CAE on real factory data.

Outperformed baseline autoencoder, one-class SVM, and isolation forest.

Adding skip connections and transformers enhanced detection performance.

Abstract

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.