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.

中文翻译：

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通过混合机器学习框架在自动化施工中进行设备活动识别和早期故障检测

现有关于自动化施工设备监控的研究主要集中在活动识别而不是故障检测上。本文提出了一种称为混合无监督和监督机器学习（HUS-ML）的新型设备活动识别和故障检测框架。HUS-ML 首先通过监督学习识别正常操作和已知故障情况。然后，应用异常检测算法来发现任何看不见的故障情况。该框架使用来自低层自动化施工系统原型的加速度测量值进行测试。HUS-ML 在活动识别和故障检测方面优于传统的机器学习方法，平均 F1 得分为 86.6%。传统方法无法检测到看不见的错误操作。HUS-ML 识别出已知的错误操作和未发现的错误操作，F1 分数分别为 98.11% 和 76.19%。通过在独立的基准数据集上对其进行验证并取得良好结果，证明了该框架的通用性。