Fast CRDNN: Towards on Site Training of Mobile Construction Machines

TL;DR

This paper introduces Fast CRDNN, a transfer learning-enabled neural network that can be trained on-site for mobile construction machines using IoT technology, improving efficiency and adaptability over traditional offline methods.

Contribution

It presents a novel on-site training approach for CRDNN using transfer learning and IoT, enabling real-time adaptation to different machines with minimal computational load.

Findings

CRDNN improves torque cycle detection accuracy by about 9%.

Transfer learning allows quick adaptation of the model to new machines.

CRDNN outperforms other state-of-the-art multivariate time series algorithms.

Abstract

The CRDNN is a combined neural network that can increase the holistic efficiency of torque based mobile working machines by about 9% by means of accurately detecting the truck loading cycles. On the one hand, it is a robust but offline learning algorithm so that it is more accurate and much quicker than the previous methods. However, on the other hand, its accuracy can not always be guaranteed because of the diversity of the mobile machines industry and the nature of the offline method. To address the problem, we utilize the transfer learning algorithm and the Internet of Things (IoT) technology. Concretely, the CRDNN is first trained by computer and then saved in the on-board ECU. In case that the pre-trained CRDNN is not suitable for the new machine, the operator can label some new data by our App connected to the on-board ECU of that machine through Bluetooth. With the newly labeled data, we can directly further train the pretrained CRDNN on the ECU without overloading since transfer learning requires less computation effort than training the networks from scratch. In our paper, we prove this idea and show that CRDNN is always competent, with the help of transfer learning and IoT technology by field experiment, even the new machine may have a different distribution. Also, we compared the performance of other SOTA multivariate time series algorithms on predicting the working state of the mobile machines, which denotes that the CRDNNs are still the most suitable solution. As a by-product, we build up a human-machine communication system to label the dataset, which can be operated by engineers without knowledge about Artificial Intelligence (AI).