Equipment activity recognition and early fault detection in automated construction through a hybrid machine learning framework

Existing studies on automated construction equipment monitoring have focused mainly on activity recognition rather than fault detection. This paper proposes a novel equipment activity recognition and fault detection framework called hybrid unsupervised and supervised machine learning (HUS-ML). HUS-ML first identifies normal operations and known faulty conditions through supervised learning. Then, an anomaly detection algorithm is applied to spot any unseen faulty conditions. The framework is tested using acceleration measurements from a low-rise automated construction system prototype. HUS-ML outperformed the conventional machine learning approach in activity recognition and fault detection with an average F1 score of 86.6%. The conventional approach failed to detect unseen faulty operations. HUS-ML identified known faulty operations and unseen faulty operations with F1 scores of 98.11% and 76.19%, respectively. The generalizability of the framework is demonstrated by validating it on an independent benchmark dataset with good results.

Automated summary

This study proposes a framework called hybrid unsupervised and supervised machine learning (HUS-ML) for equipment activity recognition and fault detection. The framework identifies normal operations and known faulty conditions through supervised learning, and utilizes an anomaly detection algorithm to spot unseen faulty conditions. Experimental results demonstrate the superiority of HUS-ML in activity recognition and fault detection, with successful identification of known and unseen faulty operations.