Simultaneous estimation for rope tension and load of cranes using motor driving signals

Abstract

Overhead crane is a product used to transport heavy objects in a factory, and advanced operating skill is required to operate safely and with less equipment failure. However, the shortage of skilled operators has been pointed out in recent years. The purpose of this paper is to develop a method for estimating tense state of rope and weight of suspended load at the same time with the aim of improving the safety of overhead cranes without relying on operator skills. When estimating multiple states using independent estimation models, the issue is the lack of computer storage space and calculation speed due to the increase of the parameters. Therefore, the authors adopted a method that simultaneously estimates several states in a single model. To estimate the tense state of rope and weight of suspended load from the driving signals of the induction motor mounted on the overhead crane, a multi-task neural network that can express the nonlinearity of motor characteristic was used. The teaching data used for learning network parameters was created by using object detection technology to quantify the transportation of markers attached on load block and suspended load. As a result, it was clarified that the rope tension state and the load can be estimated simultaneously by inputting the driving signals of the motor to the multi-task neural network. Moreover, it was shown that the rope tension probability and the estimated load increased synchronously as the motor signals change, and the tension detection was output.

Keywords

• crane
• overhead crane
• multi task learning
• deep learning
• load estimation
• state estimation