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

Fast CRDNN: Towards on Site Training of Mobile Construction Machines