Machine Activity Recognition with Augmented Self-training

Abstract: One of the most important business elements in primary and secondary industries is monitoring their equipment. Understanding the usage of heavy logistics machinery can help in realizing the potential of these machinery and improving them. With the purpose of monitoring and quantifying machine usage, Machine Activity Recognition (MAR) problems can be solved with machine learning techniques. In this project, We propose a method of augmented Self-training which collaborates Self-training and data augmentation to solve forklift trucks' MAR problem on Controller Area Network (CAN bus) data. Compared to the standard Self-training method, the augmented Self-training performs data augmentation on pseudo-labeled data to inject noise and to improve model generalization. The best student model of the augmented Self-training achieves 71.8% balanced accuracy (BA) with improvement of 3.0% from applying Supervised Learning solely (68.8% BA). In addition, Matthews Correlation Coefficient (MCC) for the augmented Self-training's best student model reaches 0.658 with an increment of 0.031, compare to an MCC of 0.627 by only applying Supervised Learning. The augmented Self-training improves model performance.