Localization of Inspection Device Along Belt Conveyors With Multiple Branches Using Deep Neural Networks

Regular inspections of belt conveyors are required to prevent the damage of transported objects. Nevertheless, inspections can be troublesome for belt conveyors composed of a plurality of belt lines with multiple branches. To improve the inspection process, an inspection device composed of an inertial measurement unit (IMU) inside a transported object was introduced in our previous study jointly with an algorithm to detect anomalies in the joints of the belt lines. When belt conveying this device for inspection, however, it is required to not only detect the anomaly but also know its position. This study presents a novel method to estimate the position of the inspection device along the belt conveyor using a deep neural network (DNN). The DNN uses the IMU data to detect seven types of features (passage through five types of joints, device stoppage and regular transport), which, when matched to data on a belt conveyor position database, can correctly be translated into positions. Additionally, the proposed method enables the detection of changes of routes along the belt conveyor that occur when a belt line branches into two output streams. To enhance the DNN feature detections, two original algorithms for DNN output post-processing are also introduced. Experiments with a complex belt conveyor demonstrate this method can successfully detect the position of the inspected device more accurately and more cost-effectively than conventional methods.