Real-time anomaly detection in construction equipment operations using unsupervised audio signal processing

Automated and non-invasive anomaly detection methods are critical for ensuring operational safety and continuity on intelligent construction sites. This study proposes a novel unsupervised audio signal processing framework for real-time monitoring of construction equipment based on their operational acoustic signatures. The proposed method relies exclusively on historical data from normal operations to characterize temporal audio patterns, enabling the detection of previously unseen anomalies without requiring labeled anomaly data for training. It extracts 39 acoustic features from raw waveform audio and reconstructs them using a temporal convolutional network autoencoder. Anomalies are identified by monitoring the reconstruction errors through a multivariate cumulative sum (MCUSUM) statistical process control chart. Upon detecting an anomaly, the method identifies contributing acoustic features via correlation maximization decomposition of MCUSUM statistics. The proposed method detected 100% of anomalies in 50 real-world slider rail tests, with an average detection time of 2.15 s post onset.