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

2025-01-08

AI Summary

To address maintenance delays and unplanned downtime in woodworking planers caused by skilled labor shortages, this paper proposes an unsupervised acoustic anomaly detection method. To overcome sensor failures in harsh sawmill environments and the poor robustness of existing audio-based detection approaches, we introduce— for the first time—the Skip-CAE, a convolutional autoencoder with skip connections, and further integrate it with a Transformer encoder-decoder architecture into a novel hybrid model: Skip-CAE Transformer. This design synergistically optimizes local feature reconstruction and global temporal modeling. Evaluated on a real-world industrial acoustic dataset from a timber processing facility, the method achieves AUC scores of 0.846 (Skip-CAE) and 0.875 (Skip-CAE Transformer), significantly outperforming DCASE baselines and mainstream one-class methods such as One-Class SVM. The proposed framework establishes a deployable, acoustic-driven diagnostic paradigm for intelligent condition monitoring of forestry machinery.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Machine Maintenance
• Anomaly Sound Detection
• Skilled Labor Shortage

Innovation

• Machine Learning
• Attention Mechanism
• Acoustic Monitoring