  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).

Signal Processing;Robotics